Warning: Permanently added '2620:52:3:1:dead:beef:cafe:c1db' (ED25519) to the list of known hosts.

You can reproduce this build on your computer by running:

  sudo dnf install copr-rpmbuild
  /usr/bin/copr-rpmbuild --verbose --drop-resultdir --task-url https://copr.fedorainfracloud.org/backend/get-build-task/9355555-fedora-rawhide-x86_64 --chroot fedora-rawhide-x86_64


Version: 1.3
PID: 2325
Logging PID: 2326
Task:
{'allow_user_ssh': False,
 'appstream': False,
 'background': True,
 'build_id': 9355555,
 'buildroot_pkgs': [],
 'chroot': 'fedora-rawhide-x86_64',
 'enable_net': False,
 'fedora_review': False,
 'git_hash': 'd9552a193d67b51834b1fcb75f2fa5def17053bf',
 'git_repo': 'https://copr-dist-git.fedorainfracloud.org/git/thrnciar/pyproject-rpm-macros-control/python-dask',
 'isolation': 'default',
 'memory_reqs': 2048,
 'package_name': 'python-dask',
 'package_version': '2025.4.1-3',
 'project_dirname': 'pyproject-rpm-macros-control',
 'project_name': 'pyproject-rpm-macros-control',
 'project_owner': 'thrnciar',
 'repo_priority': None,
 'repos': [{'baseurl': 'https://download.copr.fedorainfracloud.org/results/thrnciar/pyproject-rpm-macros-control/fedora-rawhide-x86_64/',
            'id': 'copr_base',
            'name': 'Copr repository',
            'priority': None},
           {'baseurl': 'http://kojipkgs.fedoraproject.org/repos/rawhide/latest/$basearch/',
            'id': 'http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch',
            'name': 'Additional repo http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch'}],
 'sandbox': 'thrnciar/pyproject-rpm-macros-control--https://src.fedoraproject.org/user/ksurma',
 'source_json': {},
 'source_type': None,
 'ssh_public_keys': None,
 'storage': 0,
 'submitter': 'https://src.fedoraproject.org/user/ksurma',
 'tags': [],
 'task_id': '9355555-fedora-rawhide-x86_64',
 'timeout': 18000,
 'uses_devel_repo': False,
 'with_opts': [],
 'without_opts': []}

Running: git clone https://copr-dist-git.fedorainfracloud.org/git/thrnciar/pyproject-rpm-macros-control/python-dask /var/lib/copr-rpmbuild/workspace/workdir-hmpa4x_y/python-dask --depth 500 --no-single-branch --recursive

cmd: ['git', 'clone', 'https://copr-dist-git.fedorainfracloud.org/git/thrnciar/pyproject-rpm-macros-control/python-dask', '/var/lib/copr-rpmbuild/workspace/workdir-hmpa4x_y/python-dask', '--depth', '500', '--no-single-branch', '--recursive']
cwd: .
rc: 0
stdout: 
stderr: Cloning into '/var/lib/copr-rpmbuild/workspace/workdir-hmpa4x_y/python-dask'...

Running: git checkout d9552a193d67b51834b1fcb75f2fa5def17053bf --

cmd: ['git', 'checkout', 'd9552a193d67b51834b1fcb75f2fa5def17053bf', '--']
cwd: /var/lib/copr-rpmbuild/workspace/workdir-hmpa4x_y/python-dask
rc: 0
stdout: 
stderr: Note: switching to 'd9552a193d67b51834b1fcb75f2fa5def17053bf'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at d9552a1 automatic import of python-dask

Running: dist-git-client sources

cmd: ['dist-git-client', 'sources']
cwd: /var/lib/copr-rpmbuild/workspace/workdir-hmpa4x_y/python-dask
rc: 0
tail: /var/lib/copr-rpmbuild/main.log: file truncated
stdout: 
stderr: INFO: Reading stdout from command: git rev-parse --abbrev-ref HEAD
INFO: Reading stdout from command: git rev-parse HEAD
INFO: Reading sources specification file: sources
INFO: Downloading dask-2025.4.1.tar.gz
INFO: Reading stdout from command: curl --help all
INFO: Calling: curl -H Pragma: -o dask-2025.4.1.tar.gz --location --connect-timeout 60 --retry 3 --retry-delay 10 --remote-time --show-error --fail --retry-all-errors https://copr-dist-git.fedorainfracloud.org/repo/pkgs/thrnciar/pyproject-rpm-macros-control/python-dask/dask-2025.4.1.tar.gz/md5/b0b3acf0a2d1eec6b23b457e65fdb4d8/dask-2025.4.1.tar.gz
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 10.4M  100 10.4M    0     0  24.0M      0 --:--:-- --:--:-- --:--:-- 24.0M
INFO: Reading stdout from command: md5sum dask-2025.4.1.tar.gz

Running (timeout=18000): unbuffer mock --spec /var/lib/copr-rpmbuild/workspace/workdir-hmpa4x_y/python-dask/python-dask.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-hmpa4x_y/python-dask --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1753885010.838369 -r /var/lib/copr-rpmbuild/results/configs/child.cfg
INFO: mock.py version 6.3 starting (python version = 3.13.5, NVR = mock-6.3-1.fc42), args: /usr/libexec/mock/mock --spec /var/lib/copr-rpmbuild/workspace/workdir-hmpa4x_y/python-dask/python-dask.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-hmpa4x_y/python-dask --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1753885010.838369 -r /var/lib/copr-rpmbuild/results/configs/child.cfg
Start(bootstrap): init plugins
INFO: tmpfs initialized
INFO: selinux enabled
INFO: chroot_scan: initialized
INFO: compress_logs: initialized
Finish(bootstrap): init plugins
Start: init plugins
INFO: tmpfs initialized
INFO: selinux enabled
INFO: chroot_scan: initialized
INFO: compress_logs: initialized
Finish: init plugins
INFO: Signal handler active
Start: run
INFO: Start(/var/lib/copr-rpmbuild/workspace/workdir-hmpa4x_y/python-dask/python-dask.spec)  Config(fedora-rawhide-x86_64)
Start: clean chroot
Finish: clean chroot
Mock Version: 6.3
INFO: Mock Version: 6.3
Start(bootstrap): chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-rawhide-x86_64-bootstrap-1753885010.838369/root.
INFO: calling preinit hooks
INFO: enabled root cache
INFO: enabled package manager cache
Start(bootstrap): cleaning package manager metadata
Finish(bootstrap): cleaning package manager metadata
INFO: Guessed host environment type: unknown
INFO: Using container image: registry.fedoraproject.org/fedora:rawhide
INFO: Pulling image: registry.fedoraproject.org/fedora:rawhide
INFO: Tagging container image as mock-bootstrap-cc9f2079-6de0-414b-94e0-c3402c520c98
INFO: Checking that f4185c2bbe1ceaaafd1690b431be0e6c279207962ab0556fcce48581819703d5 image matches host's architecture
INFO: Copy content of container f4185c2bbe1ceaaafd1690b431be0e6c279207962ab0556fcce48581819703d5 to /var/lib/mock/fedora-rawhide-x86_64-bootstrap-1753885010.838369/root
INFO: mounting f4185c2bbe1ceaaafd1690b431be0e6c279207962ab0556fcce48581819703d5 with podman image mount
INFO: image f4185c2bbe1ceaaafd1690b431be0e6c279207962ab0556fcce48581819703d5 as /var/lib/containers/storage/overlay/93f07b9ab01d3e57d080ce250f85509f9f68b99796ed74f75aa043b2ffd80290/merged
INFO: umounting image f4185c2bbe1ceaaafd1690b431be0e6c279207962ab0556fcce48581819703d5 (/var/lib/containers/storage/overlay/93f07b9ab01d3e57d080ce250f85509f9f68b99796ed74f75aa043b2ffd80290/merged) with podman image umount
INFO: Removing image mock-bootstrap-cc9f2079-6de0-414b-94e0-c3402c520c98
INFO: Package manager dnf5 detected and used (fallback)
INFO: Not updating bootstrap chroot, bootstrap_image_ready=True
Start(bootstrap): creating root cache
Finish(bootstrap): creating root cache
Finish(bootstrap): chroot init
Start: chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-rawhide-x86_64-1753885010.838369/root.
INFO: calling preinit hooks
INFO: enabled root cache
INFO: enabled package manager cache
Start: cleaning package manager metadata
Finish: cleaning package manager metadata
INFO: enabled HW Info plugin
INFO: Package manager dnf5 detected and used (direct choice)
INFO: Buildroot is handled by package management downloaded with a bootstrap image:
  rpm-5.99.91-2.fc43.x86_64
  rpm-sequoia-1.9.0-2.fc43.x86_64
  dnf5-5.2.15.0-3.fc43.x86_64
  dnf5-plugins-5.2.15.0-3.fc43.x86_64
Start: installing minimal buildroot with dnf5
Updating and loading repositories:
 Copr repository                        100% |  38.1 KiB/s |  45.5 KiB |  00m01s
 Additional repo http_kojipkgs_fedorapr 100% |   9.2 MiB/s |  14.4 MiB |  00m02s
 fedora                                 100% |  12.5 MiB/s |  22.9 MiB |  00m02s
Repositories loaded.
Package                            Arch   Version                    Repository                                                         Size
Installing group/module packages:
 bash                              x86_64 5.3.0-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   8.4 MiB
 bzip2                             x86_64 1.0.8-21.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  95.3 KiB
 coreutils                         x86_64 9.7-5.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   5.4 MiB
 cpio                              x86_64 2.15-6.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.1 MiB
 diffutils                         x86_64 3.12-3.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.6 MiB
 fedora-release-common             noarch 43-0.19                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  20.4 KiB
 findutils                         x86_64 1:4.10.0-6.fc43            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.8 MiB
 gawk                              x86_64 5.3.2-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.8 MiB
 glibc-minimal-langpack            x86_64 2.41.9000-24.fc43          http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   0.0   B
 grep                              x86_64 3.12-2.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.0 MiB
 gzip                              x86_64 1.13-4.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 388.8 KiB
 info                              x86_64 7.2-4.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 353.9 KiB
 patch                             x86_64 2.8-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 222.8 KiB
 redhat-rpm-config                 noarch 343-10.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 182.9 KiB
 rpm-build                         x86_64 5.99.91-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 285.5 KiB
 sed                               x86_64 4.9-5.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 857.3 KiB
 shadow-utils                      x86_64 2:4.18.0-3.fc43            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.9 MiB
 tar                               x86_64 2:1.35-6.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.9 MiB
 unzip                             x86_64 6.0-67.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 386.3 KiB
 util-linux                        x86_64 2.41.1-13.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.5 MiB
 which                             x86_64 2.23-3.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  83.5 KiB
 xz                                x86_64 1:5.8.1-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.3 MiB
Installing dependencies:
 add-determinism                   x86_64 0.6.0-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.4 MiB
 alternatives                      x86_64 1.33-2.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  62.2 KiB
 ansible-srpm-macros               noarch 1-18.1.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  35.7 KiB
 audit-libs                        x86_64 4.1.0-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 378.8 KiB
 binutils                          x86_64 2.45-1.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  26.5 MiB
 build-reproducibility-srpm-macros noarch 0.6.0-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 735.0   B
 bzip2-libs                        x86_64 1.0.8-21.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  80.6 KiB
 ca-certificates                   noarch 2024.2.69_v8.0.401-7.fc43  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.6 MiB
 coreutils-common                  x86_64 9.7-5.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  11.3 MiB
 crypto-policies                   noarch 20250714-3.gitcd6043a.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 146.9 KiB
 curl                              x86_64 8.15.0-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 473.6 KiB
 cyrus-sasl-lib                    x86_64 2.1.28-33.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.3 MiB
 debugedit                         x86_64 5.2-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 197.8 KiB
 dwz                               x86_64 0.16-2.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 287.1 KiB
 ed                                x86_64 1.22-2.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 147.1 KiB
 efi-srpm-macros                   noarch 6-4.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  40.1 KiB
 elfutils                          x86_64 0.193-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.9 MiB
 elfutils-debuginfod-client        x86_64 0.193-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  83.9 KiB
 elfutils-default-yama-scope       noarch 0.193-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.8 KiB
 elfutils-libelf                   x86_64 0.193-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.2 MiB
 elfutils-libs                     x86_64 0.193-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 683.4 KiB
 fedora-gpg-keys                   noarch 43-0.2                     http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 129.0 KiB
 fedora-release                    noarch 43-0.19                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   0.0   B
 fedora-release-identity-basic     noarch 43-0.19                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 664.0   B
 fedora-repos                      noarch 43-0.2                     http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   4.9 KiB
 fedora-repos-rawhide              noarch 43-0.2                     http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.2 KiB
 file                              x86_64 5.46-6.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 100.2 KiB
 file-libs                         x86_64 5.46-6.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  11.9 MiB
 filesystem                        x86_64 3.18-45.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 112.0   B
 filesystem-srpm-macros            noarch 3.18-45.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  38.2 KiB
 fonts-srpm-macros                 noarch 1:2.0.5-23.fc43            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  55.8 KiB
 forge-srpm-macros                 noarch 0.4.0-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  38.9 KiB
 fpc-srpm-macros                   noarch 1.3-15.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 144.0   B
 gdb-minimal                       x86_64 16.3-4.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  13.2 MiB
 gdbm-libs                         x86_64 1:1.23-10.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 129.9 KiB
 ghc-srpm-macros                   noarch 1.9.2-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 779.0   B
 glibc                             x86_64 2.41.9000-24.fc43          http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   6.7 MiB
 glibc-common                      x86_64 2.41.9000-24.fc43          http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.0 MiB
 glibc-gconv-extra                 x86_64 2.41.9000-24.fc43          http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   7.2 MiB
 gmp                               x86_64 1:6.3.0-4.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 811.2 KiB
 gnat-srpm-macros                  noarch 6-8.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.0 KiB
 gnupg2                            x86_64 2.4.8-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   6.5 MiB
 gnupg2-dirmngr                    x86_64 2.4.8-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 618.4 KiB
 gnupg2-gpg-agent                  x86_64 2.4.8-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 671.4 KiB
 gnupg2-gpgconf                    x86_64 2.4.8-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 250.0 KiB
 gnupg2-keyboxd                    x86_64 2.4.8-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 201.4 KiB
 gnupg2-verify                     x86_64 2.4.8-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 348.5 KiB
 gnutls                            x86_64 3.8.10-3.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.8 MiB
 go-srpm-macros                    noarch 3.7.0-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  61.6 KiB
 gpgverify                         noarch 2.2-3.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   8.7 KiB
 ima-evm-utils-libs                x86_64 1.6.2-6.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  60.7 KiB
 jansson                           x86_64 2.14-3.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  89.1 KiB
 java-srpm-macros                  noarch 1-7.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 870.0   B
 json-c                            x86_64 0.18-7.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  82.7 KiB
 kernel-srpm-macros                noarch 1.0-27.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.9 KiB
 keyutils-libs                     x86_64 1.6.3-6.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  54.3 KiB
 krb5-libs                         x86_64 1.21.3-7.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.3 MiB
 libacl                            x86_64 2.3.2-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  35.9 KiB
 libarchive                        x86_64 3.8.1-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 951.1 KiB
 libassuan                         x86_64 2.5.7-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 163.8 KiB
 libattr                           x86_64 2.5.2-6.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  24.4 KiB
 libblkid                          x86_64 2.41.1-13.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 262.4 KiB
 libbrotli                         x86_64 1.1.0-8.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 833.3 KiB
 libcap                            x86_64 2.76-2.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 209.1 KiB
 libcap-ng                         x86_64 0.8.5-6.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  68.9 KiB
 libcom_err                        x86_64 1.47.3-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  63.1 KiB
 libcurl                           x86_64 8.15.0-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 903.2 KiB
 libeconf                          x86_64 0.7.9-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  64.9 KiB
 libevent                          x86_64 2.1.12-16.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 883.1 KiB
 libfdisk                          x86_64 2.41.1-13.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 380.4 KiB
 libffi                            x86_64 3.5.1-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  83.6 KiB
 libfsverity                       x86_64 1.6-3.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  28.5 KiB
 libgcc                            x86_64 15.1.1-5.fc43.1            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 266.6 KiB
 libgcrypt                         x86_64 1.11.1-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.6 MiB
 libgomp                           x86_64 15.1.1-5.fc43.1            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 539.1 KiB
 libgpg-error                      x86_64 1.55-2.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 915.3 KiB
 libidn2                           x86_64 2.3.8-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 552.5 KiB
 libksba                           x86_64 1.6.7-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 398.5 KiB
 liblastlog2                       x86_64 2.41.1-13.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  33.9 KiB
 libmount                          x86_64 2.41.1-13.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 372.7 KiB
 libnghttp2                        x86_64 1.66.0-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 162.2 KiB
 libpkgconf                        x86_64 2.3.0-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  78.1 KiB
 libpsl                            x86_64 0.21.5-6.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  76.4 KiB
 libselinux                        x86_64 3.9-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 193.1 KiB
 libsemanage                       x86_64 3.9-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 308.4 KiB
 libsepol                          x86_64 3.9-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 822.0 KiB
 libsmartcols                      x86_64 2.41.1-13.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 180.5 KiB
 libssh                            x86_64 0.11.2-1.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 566.7 KiB
 libssh-config                     noarch 0.11.2-1.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 277.0   B
 libstdc++                         x86_64 15.1.1-5.fc43.1            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.8 MiB
 libtasn1                          x86_64 4.20.0-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 176.3 KiB
 libtool-ltdl                      x86_64 2.5.4-7.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  70.1 KiB
 libunistring                      x86_64 1.1-10.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.7 MiB
 libusb1                           x86_64 1.0.28-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 171.0 KiB
 libuuid                           x86_64 2.41.1-13.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  37.4 KiB
 libverto                          x86_64 0.3.2-11.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  25.4 KiB
 libxcrypt                         x86_64 4.4.38-8.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 284.5 KiB
 libxml2                           x86_64 2.12.10-3.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.7 MiB
 libzstd                           x86_64 1.5.7-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 799.9 KiB
 lua-libs                          x86_64 5.4.8-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 280.8 KiB
 lua-srpm-macros                   noarch 1-16.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.3 KiB
 lz4-libs                          x86_64 1.10.0-3.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 161.4 KiB
 mpfr                              x86_64 4.2.2-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 832.8 KiB
 ncurses-base                      noarch 6.5-7.20250614.fc43        http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 328.1 KiB
 ncurses-libs                      x86_64 6.5-7.20250614.fc43        http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 946.3 KiB
 nettle                            x86_64 3.10.1-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 790.6 KiB
 npth                              x86_64 1.8-3.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  49.6 KiB
 ocaml-srpm-macros                 noarch 11-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.9 KiB
 openblas-srpm-macros              noarch 2-20.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 112.0   B
 openldap                          x86_64 2.6.10-3.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 655.8 KiB
 openssl-libs                      x86_64 1:3.5.1-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   8.9 MiB
 p11-kit                           x86_64 0.25.5-9.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.2 MiB
 p11-kit-trust                     x86_64 0.25.5-9.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 395.5 KiB
 package-notes-srpm-macros         noarch 0.5-14.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.6 KiB
 pam-libs                          x86_64 1.7.1-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 126.8 KiB
 pcre2                             x86_64 10.45-1.fc43.1             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 697.7 KiB
 pcre2-syntax                      noarch 10.45-1.fc43.1             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 273.9 KiB
 perl-srpm-macros                  noarch 1-60.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 861.0   B
 pkgconf                           x86_64 2.3.0-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  88.5 KiB
 pkgconf-m4                        noarch 2.3.0-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  14.4 KiB
 pkgconf-pkg-config                x86_64 2.3.0-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 989.0   B
 popt                              x86_64 1.19-9.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 132.8 KiB
 publicsuffix-list-dafsa           noarch 20250616-2.fc43            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  69.1 KiB
 pyproject-srpm-macros             noarch 1.18.3-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.9 KiB
 python-srpm-macros                noarch 3.14-4.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  51.5 KiB
 qt5-srpm-macros                   noarch 5.15.17-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 500.0   B
 qt6-srpm-macros                   noarch 6.9.1-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 464.0   B
 readline                          x86_64 8.3-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 511.7 KiB
 rpm                               x86_64 5.99.91-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.0 MiB
 rpm-build-libs                    x86_64 5.99.91-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 268.4 KiB
 rpm-libs                          x86_64 5.99.91-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 933.8 KiB
 rpm-sequoia                       x86_64 1.9.0-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.5 MiB
 rpm-sign-libs                     x86_64 5.99.91-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  39.7 KiB
 rust-srpm-macros                  noarch 26.3-5.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   4.8 KiB
 setup                             noarch 2.15.0-26.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 725.0 KiB
 sqlite-libs                       x86_64 3.50.2-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.5 MiB
 systemd-libs                      x86_64 257.7-1.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.2 MiB
 systemd-standalone-sysusers       x86_64 257.7-1.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 277.3 KiB
 tpm2-tss                          x86_64 4.1.3-8.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.6 MiB
 tree-sitter-srpm-macros           noarch 0.4.2-1.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   8.3 KiB
 util-linux-core                   x86_64 2.41.1-13.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.5 MiB
 xxhash-libs                       x86_64 0.8.3-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  90.2 KiB
 xz-libs                           x86_64 1:5.8.1-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 217.8 KiB
 zig-srpm-macros                   noarch 1-5.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.1 KiB
 zip                               x86_64 3.0-44.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 694.5 KiB
 zlib-ng-compat                    x86_64 2.2.4-3.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 137.6 KiB
 zstd                              x86_64 1.5.7-2.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.7 MiB
Installing groups:
 Buildsystem building group                                                                                                                 

Transaction Summary:
 Installing:       169 packages

Total size of inbound packages is 58 MiB. Need to download 58 MiB.
After this operation, 198 MiB extra will be used (install 198 MiB, remove 0 B).
[  1/169] bzip2-0:1.0.8-21.fc43.x86_64  100% | 382.4 KiB/s |  51.6 KiB |  00m00s
[  2/169] cpio-0:2.15-6.fc43.x86_64     100% |   3.7 MiB/s | 286.6 KiB |  00m00s
[  3/169] coreutils-0:9.7-5.fc43.x86_64 100% |   5.2 MiB/s |   1.1 MiB |  00m00s
[  4/169] bash-0:5.3.0-2.fc43.x86_64    100% |   7.9 MiB/s |   1.8 MiB |  00m00s
[  5/169] diffutils-0:3.12-3.fc43.x86_6 100% |   8.7 MiB/s | 384.1 KiB |  00m00s
[  6/169] fedora-release-common-0:43-0. 100% | 635.5 KiB/s |  25.4 KiB |  00m00s
[  7/169] findutils-1:4.10.0-6.fc43.x86 100% |  11.0 MiB/s | 541.2 KiB |  00m00s
[  8/169] glibc-minimal-langpack-0:2.41 100% | 862.5 KiB/s |  35.4 KiB |  00m00s
[  9/169] gawk-0:5.3.2-2.fc43.x86_64    100% |  21.4 MiB/s |   1.1 MiB |  00m00s
[ 10/169] grep-0:3.12-2.fc43.x86_64     100% |   6.0 MiB/s | 289.1 KiB |  00m00s
[ 11/169] gzip-0:1.13-4.fc43.x86_64     100% |   4.1 MiB/s | 164.0 KiB |  00m00s
[ 12/169] info-0:7.2-4.fc43.x86_64      100% |   4.5 MiB/s | 182.8 KiB |  00m00s
[ 13/169] patch-0:2.8-2.fc43.x86_64     100% |   2.8 MiB/s | 113.8 KiB |  00m00s
[ 14/169] redhat-rpm-config-0:343-10.fc 100% |   1.9 MiB/s |  73.1 KiB |  00m00s
[ 15/169] rpm-build-0:5.99.91-2.fc43.x8 100% |   3.1 MiB/s | 127.5 KiB |  00m00s
[ 16/169] sed-0:4.9-5.fc43.x86_64       100% |   6.3 MiB/s | 308.6 KiB |  00m00s
[ 17/169] shadow-utils-2:4.18.0-3.fc43. 100% |   7.1 MiB/s |   1.2 MiB |  00m00s
[ 18/169] tar-2:1.35-6.fc43.x86_64      100% |   4.8 MiB/s | 847.8 KiB |  00m00s
[ 19/169] which-0:2.23-3.fc43.x86_64    100% | 802.5 KiB/s |  41.7 KiB |  00m00s
[ 20/169] util-linux-0:2.41.1-13.fc43.x 100% |  11.8 MiB/s |   1.1 MiB |  00m00s
[ 21/169] unzip-0:6.0-67.fc43.x86_64    100% | 798.5 KiB/s | 183.7 KiB |  00m00s
[ 22/169] xz-1:5.8.1-2.fc43.x86_64      100% |   9.2 MiB/s | 556.6 KiB |  00m00s
[ 23/169] xz-libs-1:5.8.1-2.fc43.x86_64 100% |   2.9 MiB/s | 112.9 KiB |  00m00s
[ 24/169] audit-libs-0:4.1.0-3.fc43.x86 100% |   3.1 MiB/s | 138.4 KiB |  00m00s
[ 25/169] glibc-0:2.41.9000-24.fc43.x86 100% |  25.2 MiB/s |   2.2 MiB |  00m00s
[ 26/169] libblkid-0:2.41.1-13.fc43.x86 100% |   3.1 MiB/s | 122.9 KiB |  00m00s
[ 27/169] libcap-ng-0:0.8.5-6.fc43.x86_ 100% | 844.3 KiB/s |  32.1 KiB |  00m00s
[ 28/169] libfdisk-0:2.41.1-13.fc43.x86 100% |   4.0 MiB/s | 160.8 KiB |  00m00s
[ 29/169] filesystem-0:3.18-45.fc43.x86 100% |  11.1 MiB/s |   1.3 MiB |  00m00s
[ 30/169] libgcc-0:15.1.1-5.fc43.1.x86_ 100% |   3.2 MiB/s | 130.2 KiB |  00m00s
[ 31/169] liblastlog2-0:2.41.1-13.fc43. 100% | 599.1 KiB/s |  22.8 KiB |  00m00s
[ 32/169] libmount-0:2.41.1-13.fc43.x86 100% |   3.9 MiB/s | 162.1 KiB |  00m00s
[ 33/169] libselinux-0:3.9-2.fc43.x86_6 100% |   2.5 MiB/s |  97.2 KiB |  00m00s
[ 34/169] libsmartcols-0:2.41.1-13.fc43 100% |   1.9 MiB/s |  83.6 KiB |  00m00s
[ 35/169] libuuid-0:2.41.1-13.fc43.x86_ 100% | 517.6 KiB/s |  25.9 KiB |  00m00s
[ 36/169] pam-libs-0:1.7.1-3.fc43.x86_6 100% |   1.2 MiB/s |  57.5 KiB |  00m00s
[ 37/169] readline-0:8.3-2.fc43.x86_64  100% |   5.1 MiB/s | 224.6 KiB |  00m00s
[ 38/169] systemd-libs-0:257.7-1.fc43.x 100% |  16.4 MiB/s | 789.7 KiB |  00m00s
[ 39/169] util-linux-core-0:2.41.1-13.f 100% |   8.8 MiB/s | 534.7 KiB |  00m00s
[ 40/169] ncurses-libs-0:6.5-7.20250614 100% |   2.2 MiB/s | 332.7 KiB |  00m00s
[ 41/169] zlib-ng-compat-0:2.2.4-3.fc43 100% |   2.0 MiB/s |  79.3 KiB |  00m00s
[ 42/169] libacl-0:2.3.2-4.fc43.x86_64  100% | 656.2 KiB/s |  24.3 KiB |  00m00s
[ 43/169] bzip2-libs-0:1.0.8-21.fc43.x8 100% |   1.1 MiB/s |  43.1 KiB |  00m00s
[ 44/169] libcap-0:2.76-2.fc43.x86_64   100% |   2.2 MiB/s |  86.9 KiB |  00m00s
[ 45/169] libeconf-0:0.7.9-2.fc43.x86_6 100% | 902.8 KiB/s |  35.2 KiB |  00m00s
[ 46/169] libsemanage-0:3.9-2.fc43.x86_ 100% |   3.1 MiB/s | 123.4 KiB |  00m00s
[ 47/169] libxcrypt-0:4.4.38-8.fc43.x86 100% |   3.3 MiB/s | 127.0 KiB |  00m00s
[ 48/169] setup-0:2.15.0-26.fc43.noarch 100% |   3.7 MiB/s | 151.2 KiB |  00m00s
[ 49/169] debugedit-0:5.2-2.fc43.x86_64 100% |   1.7 MiB/s |  80.6 KiB |  00m00s
[ 50/169] elfutils-0:0.193-3.fc43.x86_6 100% |   8.8 MiB/s | 566.1 KiB |  00m00s
[ 51/169] elfutils-libelf-0:0.193-3.fc4 100% |   4.1 MiB/s | 207.8 KiB |  00m00s
[ 52/169] file-0:5.46-6.fc43.x86_64     100% | 994.6 KiB/s |  48.7 KiB |  00m00s
[ 53/169] libarchive-0:3.8.1-3.fc43.x86 100% |   5.9 MiB/s | 421.1 KiB |  00m00s
[ 54/169] pkgconf-pkg-config-0:2.3.0-3. 100% | 174.7 KiB/s |   9.6 KiB |  00m00s
[ 55/169] binutils-0:2.45-1.fc43.x86_64 100% |  22.9 MiB/s |   5.8 MiB |  00m00s
[ 56/169] libstdc++-0:15.1.1-5.fc43.1.x 100% |   8.1 MiB/s | 917.9 KiB |  00m00s
[ 57/169] rpm-build-libs-0:5.99.91-2.fc 100% |   2.5 MiB/s | 127.4 KiB |  00m00s
[ 58/169] rpm-0:5.99.91-2.fc43.x86_64   100% |   7.3 MiB/s | 532.9 KiB |  00m00s
[ 59/169] rpm-libs-0:5.99.91-2.fc43.x86 100% |   6.6 MiB/s | 399.6 KiB |  00m00s
[ 60/169] zstd-0:1.5.7-2.fc43.x86_64    100% |   7.9 MiB/s | 485.9 KiB |  00m00s
[ 61/169] curl-0:8.15.0-2.fc43.x86_64   100% |   5.6 MiB/s | 233.7 KiB |  00m00s
[ 62/169] ansible-srpm-macros-0:1-18.1. 100% | 442.4 KiB/s |  19.9 KiB |  00m00s
[ 63/169] popt-0:1.19-9.fc43.x86_64     100% | 250.5 KiB/s |  59.1 KiB |  00m00s
[ 64/169] dwz-0:0.16-2.fc43.x86_64      100% |   2.6 MiB/s | 135.5 KiB |  00m00s
[ 65/169] glibc-gconv-extra-0:2.41.9000 100% |   9.6 MiB/s |   1.5 MiB |  00m00s
[ 66/169] efi-srpm-macros-0:6-4.fc43.no 100% | 457.0 KiB/s |  22.4 KiB |  00m00s
[ 67/169] filesystem-srpm-macros-0:3.18 100% | 575.2 KiB/s |  25.9 KiB |  00m00s
[ 68/169] build-reproducibility-srpm-ma 100% |  83.9 KiB/s |  11.8 KiB |  00m00s
[ 69/169] fonts-srpm-macros-1:2.0.5-23. 100% | 590.7 KiB/s |  27.2 KiB |  00m00s
[ 70/169] forge-srpm-macros-0:0.4.0-3.f 100% | 467.2 KiB/s |  20.1 KiB |  00m00s
[ 71/169] fpc-srpm-macros-0:1.3-15.fc43 100% | 183.5 KiB/s |   7.9 KiB |  00m00s
[ 72/169] ghc-srpm-macros-0:1.9.2-3.fc4 100% | 194.4 KiB/s |   8.7 KiB |  00m00s
[ 73/169] gnat-srpm-macros-0:6-8.fc43.n 100% | 192.9 KiB/s |   8.5 KiB |  00m00s
[ 74/169] go-srpm-macros-0:3.7.0-4.fc43 100% | 598.7 KiB/s |  28.1 KiB |  00m00s
[ 75/169] java-srpm-macros-0:1-7.fc43.n 100% | 184.7 KiB/s |   7.9 KiB |  00m00s
[ 76/169] kernel-srpm-macros-0:1.0-27.f 100% | 217.6 KiB/s |   8.9 KiB |  00m00s
[ 77/169] lua-srpm-macros-0:1-16.fc43.n 100% | 236.7 KiB/s |   8.8 KiB |  00m00s
[ 78/169] ocaml-srpm-macros-0:11-2.fc43 100% | 243.7 KiB/s |   9.3 KiB |  00m00s
[ 79/169] openblas-srpm-macros-0:2-20.f 100% | 205.3 KiB/s |   7.6 KiB |  00m00s
[ 80/169] package-notes-srpm-macros-0:0 100% | 242.8 KiB/s |   9.0 KiB |  00m00s
[ 81/169] perl-srpm-macros-0:1-60.fc43. 100% | 224.0 KiB/s |   8.3 KiB |  00m00s
[ 82/169] pyproject-srpm-macros-0:1.18. 100% | 361.3 KiB/s |  13.4 KiB |  00m00s
[ 83/169] python-srpm-macros-0:3.14-4.f 100% | 614.4 KiB/s |  23.3 KiB |  00m00s
[ 84/169] qt5-srpm-macros-0:5.15.17-2.f 100% | 234.1 KiB/s |   8.7 KiB |  00m00s
[ 85/169] qt6-srpm-macros-0:6.9.1-2.fc4 100% | 252.2 KiB/s |   9.3 KiB |  00m00s
[ 86/169] rust-srpm-macros-0:26.3-5.fc4 100% | 302.2 KiB/s |  11.2 KiB |  00m00s
[ 87/169] tree-sitter-srpm-macros-0:0.4 100% | 360.8 KiB/s |  13.4 KiB |  00m00s
[ 88/169] zig-srpm-macros-0:1-5.fc43.no 100% | 222.0 KiB/s |   8.4 KiB |  00m00s
[ 89/169] zip-0:3.0-44.fc43.x86_64      100% |   6.4 MiB/s | 261.6 KiB |  00m00s
[ 90/169] pkgconf-0:2.3.0-3.fc43.x86_64 100% |   1.1 MiB/s |  44.6 KiB |  00m00s
[ 91/169] pkgconf-m4-0:2.3.0-3.fc43.noa 100% | 376.0 KiB/s |  13.9 KiB |  00m00s
[ 92/169] libpkgconf-0:2.3.0-3.fc43.x86 100% | 971.7 KiB/s |  37.9 KiB |  00m00s
[ 93/169] ed-0:1.22-2.fc43.x86_64       100% |   2.1 MiB/s |  83.2 KiB |  00m00s
[ 94/169] libattr-0:2.5.2-6.fc43.x86_64 100% | 457.8 KiB/s |  17.9 KiB |  00m00s
[ 95/169] ncurses-base-0:6.5-7.20250614 100% |   1.4 MiB/s |  63.7 KiB |  00m00s
[ 96/169] pcre2-0:10.45-1.fc43.1.x86_64 100% |   2.0 MiB/s | 261.9 KiB |  00m00s
[ 97/169] libsepol-0:3.9-2.fc43.x86_64  100% |   1.9 MiB/s | 345.4 KiB |  00m00s
[ 98/169] sqlite-libs-0:3.50.2-2.fc43.x 100% |   4.9 MiB/s | 760.5 KiB |  00m00s
[ 99/169] lz4-libs-0:1.10.0-3.fc43.x86_ 100% |   1.7 MiB/s |  78.0 KiB |  00m00s
[100/169] libzstd-0:1.5.7-2.fc43.x86_64 100% |   3.6 MiB/s | 314.6 KiB |  00m00s
[101/169] libxml2-0:2.12.10-3.fc43.x86_ 100% |   5.1 MiB/s | 691.9 KiB |  00m00s
[102/169] glibc-common-0:2.41.9000-24.f 100% |   4.0 MiB/s | 317.4 KiB |  00m00s
[103/169] gmp-1:6.3.0-4.fc43.x86_64     100% |   4.2 MiB/s | 319.3 KiB |  00m00s
[104/169] mpfr-0:4.2.2-2.fc43.x86_64    100% |   4.4 MiB/s | 347.0 KiB |  00m00s
[105/169] fedora-repos-0:43-0.2.noarch  100% | 249.4 KiB/s |   9.2 KiB |  00m00s
[106/169] openssl-libs-1:3.5.1-2.fc43.x 100% |   9.9 MiB/s |   2.6 MiB |  00m00s
[107/169] elfutils-debuginfod-client-0: 100% |   1.1 MiB/s |  46.8 KiB |  00m00s
[108/169] file-libs-0:5.46-6.fc43.x86_6 100% |   5.6 MiB/s | 849.5 KiB |  00m00s
[109/169] elfutils-libs-0:0.193-3.fc43. 100% |   4.6 MiB/s | 269.7 KiB |  00m00s
[110/169] alternatives-0:1.33-2.fc43.x8 100% |   1.0 MiB/s |  40.7 KiB |  00m00s
[111/169] add-determinism-0:0.6.0-2.fc4 100% |   8.2 MiB/s | 919.3 KiB |  00m00s
[112/169] jansson-0:2.14-3.fc43.x86_64  100% |   1.2 MiB/s |  45.3 KiB |  00m00s
[113/169] lua-libs-0:5.4.8-2.fc43.x86_6 100% |   3.3 MiB/s | 131.7 KiB |  00m00s
[114/169] libgomp-0:15.1.1-5.fc43.1.x86 100% |   4.2 MiB/s | 368.1 KiB |  00m00s
[115/169] rpm-sequoia-0:1.9.0-2.fc43.x8 100% |   7.7 MiB/s | 933.3 KiB |  00m00s
[116/169] rpm-sign-libs-0:5.99.91-2.fc4 100% | 573.6 KiB/s |  28.1 KiB |  00m00s
[117/169] pcre2-syntax-0:10.45-1.fc43.1 100% |   3.5 MiB/s | 161.7 KiB |  00m00s
[118/169] coreutils-common-0:9.7-5.fc43 100% |   6.4 MiB/s |   2.1 MiB |  00m00s
[119/169] crypto-policies-0:20250714-3. 100% |   1.8 MiB/s |  74.8 KiB |  00m00s
[120/169] fedora-gpg-keys-0:43-0.2.noar 100% |   3.2 MiB/s | 125.8 KiB |  00m00s
[121/169] fedora-repos-rawhide-0:43-0.2 100% | 237.9 KiB/s |   8.8 KiB |  00m00s
[122/169] elfutils-default-yama-scope-0 100% | 335.8 KiB/s |  12.4 KiB |  00m00s
[123/169] json-c-0:0.18-7.fc43.x86_64   100% |   1.2 MiB/s |  45.0 KiB |  00m00s
[124/169] libffi-0:3.5.1-2.fc43.x86_64  100% |   1.0 MiB/s |  40.9 KiB |  00m00s
[125/169] ca-certificates-0:2024.2.69_v 100% |   6.1 MiB/s | 944.0 KiB |  00m00s
[126/169] p11-kit-0:0.25.5-9.fc43.x86_6 100% |   3.9 MiB/s | 474.4 KiB |  00m00s
[127/169] p11-kit-trust-0:0.25.5-9.fc43 100% | 919.0 KiB/s | 132.3 KiB |  00m00s
[128/169] ima-evm-utils-libs-0:1.6.2-6. 100% | 651.6 KiB/s |  29.3 KiB |  00m00s
[129/169] libfsverity-0:1.6-3.fc43.x86_ 100% | 351.5 KiB/s |  18.6 KiB |  00m00s
[130/169] gpgverify-0:2.2-3.fc43.noarch 100% | 198.3 KiB/s |  11.1 KiB |  00m00s
[131/169] libtasn1-0:4.20.0-2.fc43.x86_ 100% |   1.3 MiB/s |  74.5 KiB |  00m00s
[132/169] tpm2-tss-0:4.1.3-8.fc43.x86_6 100% |   2.6 MiB/s | 421.3 KiB |  00m00s
[133/169] libassuan-0:2.5.7-4.fc43.x86_ 100% |   1.7 MiB/s |  67.4 KiB |  00m00s
[134/169] gnupg2-verify-0:2.4.8-4.fc43. 100% | 877.7 KiB/s | 171.2 KiB |  00m00s
[135/169] libgcrypt-0:1.11.1-2.fc43.x86 100% |   4.3 MiB/s | 595.8 KiB |  00m00s
[136/169] libgpg-error-0:1.55-2.fc43.x8 100% |   2.5 MiB/s | 239.1 KiB |  00m00s
[137/169] gnupg2-0:2.4.8-4.fc43.x86_64  100% |   3.0 MiB/s |   1.6 MiB |  00m01s
[138/169] npth-0:1.8-3.fc43.x86_64      100% | 693.4 KiB/s |  25.7 KiB |  00m00s
[139/169] gnupg2-gpg-agent-0:2.4.8-4.fc 100% |   3.6 MiB/s | 272.9 KiB |  00m00s
[140/169] gnupg2-gpgconf-0:2.4.8-4.fc43 100% |   2.0 MiB/s | 115.0 KiB |  00m00s
[141/169] gnupg2-dirmngr-0:2.4.8-4.fc43 100% |   2.4 MiB/s | 274.6 KiB |  00m00s
[142/169] libusb1-0:1.0.28-2.fc43.x86_6 100% |   1.9 MiB/s |  79.3 KiB |  00m00s
[143/169] gnupg2-keyboxd-0:2.4.8-4.fc43 100% |   1.6 MiB/s |  94.7 KiB |  00m00s
[144/169] libksba-0:1.6.7-4.fc43.x86_64 100% |   1.9 MiB/s | 160.4 KiB |  00m00s
[145/169] openldap-0:2.6.10-3.fc43.x86_ 100% |   3.2 MiB/s | 259.1 KiB |  00m00s
[146/169] libevent-0:2.1.12-16.fc43.x86 100% |   3.2 MiB/s | 257.8 KiB |  00m00s
[147/169] libtool-ltdl-0:2.5.4-7.fc43.x 100% | 862.7 KiB/s |  36.2 KiB |  00m00s
[148/169] libidn2-0:2.3.8-2.fc43.x86_64 100% |   2.4 MiB/s | 168.9 KiB |  00m00s
[149/169] cyrus-sasl-lib-0:2.1.28-33.fc 100% |   3.1 MiB/s | 787.9 KiB |  00m00s
[150/169] gnutls-0:3.8.10-3.fc43.x86_64 100% |   3.7 MiB/s |   1.4 MiB |  00m00s
[151/169] libunistring-0:1.1-10.fc43.x8 100% |   3.1 MiB/s | 542.9 KiB |  00m00s
[152/169] gdbm-libs-1:1.23-10.fc43.x86_ 100% | 597.5 KiB/s |  56.8 KiB |  00m00s
[153/169] fedora-release-0:43-0.19.noar 100% | 358.2 KiB/s |  14.3 KiB |  00m00s
[154/169] systemd-standalone-sysusers-0 100% |   2.3 MiB/s | 134.8 KiB |  00m00s
[155/169] nettle-0:3.10.1-2.fc43.x86_64 100% |   1.8 MiB/s | 424.2 KiB |  00m00s
[156/169] fedora-release-identity-basic 100% | 359.5 KiB/s |  15.1 KiB |  00m00s
[157/169] xxhash-libs-0:0.8.3-3.fc43.x8 100% | 620.7 KiB/s |  38.5 KiB |  00m00s
[158/169] krb5-libs-0:1.21.3-7.fc43.x86 100% |   3.4 MiB/s | 754.8 KiB |  00m00s
[159/169] libcurl-0:8.15.0-2.fc43.x86_6 100% |   1.3 MiB/s | 404.3 KiB |  00m00s
[160/169] libbrotli-0:1.1.0-8.fc43.x86_ 100% |   3.6 MiB/s | 339.0 KiB |  00m00s
[161/169] libpsl-0:0.21.5-6.fc43.x86_64 100% |   1.6 MiB/s |  65.0 KiB |  00m00s
[162/169] libnghttp2-0:1.66.0-2.fc43.x8 100% | 966.7 KiB/s |  72.5 KiB |  00m00s
[163/169] keyutils-libs-0:1.6.3-6.fc43. 100% | 783.8 KiB/s |  31.4 KiB |  00m00s
[164/169] libssh-0:0.11.2-1.fc43.x86_64 100% |   2.7 MiB/s | 232.8 KiB |  00m00s
[165/169] libcom_err-0:1.47.3-2.fc43.x8 100% | 724.1 KiB/s |  26.8 KiB |  00m00s
[166/169] libverto-0:0.3.2-11.fc43.x86_ 100% | 544.1 KiB/s |  20.7 KiB |  00m00s
[167/169] libssh-config-0:0.11.2-1.fc43 100% | 239.2 KiB/s |   8.9 KiB |  00m00s
[168/169] publicsuffix-list-dafsa-0:202 100% |   1.5 MiB/s |  59.2 KiB |  00m00s
[169/169] gdb-minimal-0:16.3-4.fc43.x86 100% |   4.0 MiB/s |   4.4 MiB |  00m01s
--------------------------------------------------------------------------------
[169/169] Total                         100% |  11.1 MiB/s |  58.3 MiB |  00m05s
Running transaction
[  1/171] Verify package files          100% | 686.0   B/s | 169.0   B |  00m00s
[  2/171] Prepare transaction           100% |   1.8 KiB/s | 169.0   B |  00m00s
[  3/171] Installing libgcc-0:15.1.1-5. 100% | 131.0 MiB/s | 268.3 KiB |  00m00s
[  4/171] Installing publicsuffix-list- 100% |  68.2 MiB/s |  69.8 KiB |  00m00s
[  5/171] Installing libssh-config-0:0. 100% |   0.0   B/s | 816.0   B |  00m00s
[  6/171] Installing fedora-release-ide 100% |   0.0   B/s | 920.0   B |  00m00s
[  7/171] Installing fedora-repos-rawhi 100% |   2.4 MiB/s |   2.4 KiB |  00m00s
[  8/171] Installing fedora-gpg-keys-0: 100% |  13.2 MiB/s | 175.9 KiB |  00m00s
[  9/171] Installing fedora-repos-0:43- 100% |   0.0   B/s |   5.7 KiB |  00m00s
[ 10/171] Installing fedora-release-com 100% |  12.1 MiB/s |  24.8 KiB |  00m00s
[ 11/171] Installing fedora-release-0:4 100% |   4.8 KiB/s | 124.0   B |  00m00s
>>> Running sysusers scriptlet: setup-0:2.15.0-26.fc43.noarch                   
>>> Finished sysusers scriptlet: setup-0:2.15.0-26.fc43.noarch                  
>>> Scriptlet output:                                                           
>>> Creating group 'adm' with GID 4.                                            
>>> Creating group 'audio' with GID 63.                                         
>>> Creating group 'cdrom' with GID 11.                                         
>>> Creating group 'clock' with GID 103.                                        
>>> Creating group 'dialout' with GID 18.                                       
>>> Creating group 'disk' with GID 6.                                           
>>> Creating group 'floppy' with GID 19.                                        
>>> Creating group 'ftp' with GID 50.                                           
>>> Creating group 'games' with GID 20.                                         
>>> Creating group 'input' with GID 104.                                        
>>> Creating group 'kmem' with GID 9.                                           
>>> Creating group 'kvm' with GID 36.                                           
>>> Creating group 'lock' with GID 54.                                          
>>> Creating group 'lp' with GID 7.                                             
>>> Creating group 'mail' with GID 12.                                          
>>> Creating group 'man' with GID 15.                                           
>>> Creating group 'mem' with GID 8.                                            
>>> Creating group 'nobody' with GID 65534.                                     
>>> Creating group 'render' with GID 105.                                       
>>> Creating group 'root' with GID 0.                                           
>>> Creating group 'sgx' with GID 106.                                          
>>> Creating group 'sys' with GID 3.                                            
>>> Creating group 'tape' with GID 33.                                          
>>> Creating group 'tty' with GID 5.                                            
>>> Creating group 'users' with GID 100.                                        
>>> Creating group 'utmp' with GID 22.                                          
>>> Creating group 'video' with GID 39.                                         
>>> Creating group 'wheel' with GID 10.                                         
>>> Creating user 'adm' (adm) with UID 3 and GID 4.                             
>>> Creating group 'bin' with GID 1.                                            
>>> Creating user 'bin' (bin) with UID 1 and GID 1.                             
>>> Creating group 'daemon' with GID 2.                                         
>>> Creating user 'daemon' (daemon) with UID 2 and GID 2.                       
>>> Creating user 'ftp' (FTP User) with UID 14 and GID 50.                      
>>> Creating user 'games' (games) with UID 12 and GID 100.                      
>>> Creating user 'halt' (halt) with UID 7 and GID 0.                           
>>> Creating user 'lp' (lp) with UID 4 and GID 7.                               
>>> Creating user 'mail' (mail) with UID 8 and GID 12.                          
>>> Creating user 'nobody' (Kernel Overflow User) with UID 65534 and GID 65534. 
>>> Creating user 'operator' (operator) with UID 11 and GID 0.                  
>>> Creating user 'root' (Super User) with UID 0 and GID 0.                     
>>> Creating user 'shutdown' (shutdown) with UID 6 and GID 0.                   
>>> Creating user 'sync' (sync) with UID 5 and GID 0.                           
>>>                                                                             
[ 12/171] Installing setup-0:2.15.0-26. 100% |  39.6 MiB/s | 730.6 KiB |  00m00s
>>> [RPM] /etc/hosts created as /etc/hosts.rpmnew                               
[ 13/171] Installing filesystem-0:3.18- 100% |   1.3 MiB/s | 212.5 KiB |  00m00s
[ 14/171] Installing pcre2-syntax-0:10. 100% |  90.0 MiB/s | 276.4 KiB |  00m00s
[ 15/171] Installing coreutils-common-0 100% | 188.2 MiB/s |  11.3 MiB |  00m00s
[ 16/171] Installing ncurses-base-0:6.5 100% |  34.5 MiB/s | 353.5 KiB |  00m00s
[ 17/171] Installing bash-0:5.3.0-2.fc4 100% | 162.1 MiB/s |   8.4 MiB |  00m00s
[ 18/171] Installing glibc-common-0:2.4 100% |  48.6 MiB/s |   1.0 MiB |  00m00s
[ 19/171] Installing glibc-gconv-extra- 100% | 123.9 MiB/s |   7.3 MiB |  00m00s
[ 20/171] Installing glibc-0:2.41.9000- 100% | 104.6 MiB/s |   6.7 MiB |  00m00s
[ 21/171] Installing ncurses-libs-0:6.5 100% | 155.1 MiB/s | 952.8 KiB |  00m00s
[ 22/171] Installing glibc-minimal-lang 100% |   0.0   B/s | 124.0   B |  00m00s
[ 23/171] Installing zlib-ng-compat-0:2 100% | 135.2 MiB/s | 138.4 KiB |  00m00s
[ 24/171] Installing bzip2-libs-0:1.0.8 100% |  79.8 MiB/s |  81.7 KiB |  00m00s
[ 25/171] Installing libgpg-error-0:1.5 100% |  42.8 MiB/s | 921.1 KiB |  00m00s
[ 26/171] Installing libstdc++-0:15.1.1 100% | 218.8 MiB/s |   2.8 MiB |  00m00s
[ 27/171] Installing xz-libs-1:5.8.1-2. 100% | 213.8 MiB/s | 218.9 KiB |  00m00s
[ 28/171] Installing libassuan-0:2.5.7- 100% | 161.7 MiB/s | 165.6 KiB |  00m00s
[ 29/171] Installing libgcrypt-0:1.11.1 100% | 262.5 MiB/s |   1.6 MiB |  00m00s
[ 30/171] Installing readline-0:8.3-2.f 100% | 167.3 MiB/s | 513.9 KiB |  00m00s
[ 31/171] Installing libuuid-0:2.41.1-1 100% |  37.6 MiB/s |  38.5 KiB |  00m00s
[ 32/171] Installing gmp-1:6.3.0-4.fc43 100% | 158.9 MiB/s | 813.5 KiB |  00m00s
[ 33/171] Installing popt-0:1.19-9.fc43 100% |  34.0 MiB/s | 139.4 KiB |  00m00s
[ 34/171] Installing npth-0:1.8-3.fc43. 100% |  49.5 MiB/s |  50.7 KiB |  00m00s
[ 35/171] Installing libblkid-0:2.41.1- 100% | 128.7 MiB/s | 263.5 KiB |  00m00s
[ 36/171] Installing libxcrypt-0:4.4.38 100% |  70.1 MiB/s | 287.2 KiB |  00m00s
[ 37/171] Installing sqlite-libs-0:3.50 100% | 252.7 MiB/s |   1.5 MiB |  00m00s
[ 38/171] Installing libzstd-0:1.5.7-2. 100% | 156.5 MiB/s | 801.1 KiB |  00m00s
[ 39/171] Installing elfutils-libelf-0: 100% | 194.4 MiB/s |   1.2 MiB |  00m00s
[ 40/171] Installing gnupg2-gpgconf-0:2 100% |  15.4 MiB/s | 252.0 KiB |  00m00s
[ 41/171] Installing libattr-0:2.5.2-6. 100% |  24.8 MiB/s |  25.4 KiB |  00m00s
[ 42/171] Installing libacl-0:2.3.2-4.f 100% |  35.9 MiB/s |  36.8 KiB |  00m00s
[ 43/171] Installing libtasn1-0:4.20.0- 100% | 173.9 MiB/s | 178.1 KiB |  00m00s
[ 44/171] Installing libunistring-0:1.1 100% | 191.9 MiB/s |   1.7 MiB |  00m00s
[ 45/171] Installing libidn2-0:2.3.8-2. 100% |  21.8 MiB/s | 558.7 KiB |  00m00s
[ 46/171] Installing crypto-policies-0: 100% |  15.3 MiB/s | 172.0 KiB |  00m00s
[ 47/171] Installing dwz-0:0.16-2.fc43. 100% |  17.6 MiB/s | 288.5 KiB |  00m00s
[ 48/171] Installing gnupg2-verify-0:2. 100% |  24.4 MiB/s | 349.9 KiB |  00m00s
[ 49/171] Installing mpfr-0:4.2.2-2.fc4 100% | 135.8 MiB/s | 834.4 KiB |  00m00s
[ 50/171] Installing gawk-0:5.3.2-2.fc4 100% |  69.8 MiB/s |   1.8 MiB |  00m00s
[ 51/171] Installing libksba-0:1.6.7-4. 100% | 130.6 MiB/s | 401.1 KiB |  00m00s
[ 52/171] Installing unzip-0:6.0-67.fc4 100% |  22.4 MiB/s | 389.8 KiB |  00m00s
[ 53/171] Installing file-libs-0:5.46-6 100% | 312.0 MiB/s |  11.9 MiB |  00m00s
[ 54/171] Installing file-0:5.46-6.fc43 100% |   7.6 MiB/s | 101.7 KiB |  00m00s
[ 55/171] Installing libcap-ng-0:0.8.5- 100% |  69.2 MiB/s |  70.8 KiB |  00m00s
[ 56/171] Installing audit-libs-0:4.1.0 100% | 124.2 MiB/s | 381.5 KiB |  00m00s
[ 57/171] Installing libsmartcols-0:2.4 100% | 177.2 MiB/s | 181.5 KiB |  00m00s
[ 58/171] Installing libeconf-0:0.7.9-2 100% |  65.0 MiB/s |  66.5 KiB |  00m00s
[ 59/171] Installing pam-libs-0:1.7.1-3 100% |  25.2 MiB/s | 129.0 KiB |  00m00s
[ 60/171] Installing libcap-0:2.76-2.fc 100% |  13.1 MiB/s | 214.3 KiB |  00m00s
[ 61/171] Installing systemd-libs-0:257 100% | 202.9 MiB/s |   2.2 MiB |  00m00s
[ 62/171] Installing libsepol-0:3.9-2.f 100% | 160.7 MiB/s | 822.9 KiB |  00m00s
[ 63/171] Installing pcre2-0:10.45-1.fc 100% | 227.6 MiB/s | 699.1 KiB |  00m00s
[ 64/171] Installing libselinux-0:3.9-2 100% |  94.9 MiB/s | 194.4 KiB |  00m00s
[ 65/171] Installing grep-0:3.12-2.fc43 100% |  43.6 MiB/s |   1.0 MiB |  00m00s
[ 66/171] Installing findutils-1:4.10.0 100% |  71.5 MiB/s |   1.9 MiB |  00m00s
[ 67/171] Installing sed-0:4.9-5.fc43.x 100% |  38.4 MiB/s | 865.5 KiB |  00m00s
[ 68/171] Installing xz-1:5.8.1-2.fc43. 100% |  51.2 MiB/s |   1.3 MiB |  00m00s
[ 69/171] Installing libmount-0:2.41.1- 100% | 182.5 MiB/s | 373.8 KiB |  00m00s
[ 70/171] Installing lz4-libs-0:1.10.0- 100% | 158.6 MiB/s | 162.5 KiB |  00m00s
[ 71/171] Installing alternatives-0:1.3 100% |   4.8 MiB/s |  63.8 KiB |  00m00s
[ 72/171] Installing lua-libs-0:5.4.8-2 100% | 137.6 MiB/s | 281.9 KiB |  00m00s
[ 73/171] Installing json-c-0:0.18-7.fc 100% |  82.0 MiB/s |  84.0 KiB |  00m00s
[ 74/171] Installing libffi-0:3.5.1-2.f 100% |  27.7 MiB/s |  85.0 KiB |  00m00s
[ 75/171] Installing p11-kit-0:0.25.5-9 100% |  72.8 MiB/s |   2.2 MiB |  00m00s
[ 76/171] Installing p11-kit-trust-0:0. 100% |  10.2 MiB/s | 397.1 KiB |  00m00s
[ 77/171] Installing openssl-libs-1:3.5 100% | 207.0 MiB/s |   8.9 MiB |  00m00s
[ 78/171] Installing coreutils-0:9.7-5. 100% |  78.9 MiB/s |   5.4 MiB |  00m00s
[ 79/171] Installing ca-certificates-0: 100% |   1.1 MiB/s |   2.4 MiB |  00m02s
[ 80/171] Installing gzip-0:1.13-4.fc43 100% |  22.7 MiB/s | 394.4 KiB |  00m00s
[ 81/171] Installing rpm-sequoia-0:1.9. 100% | 247.8 MiB/s |   2.5 MiB |  00m00s
[ 82/171] Installing libfsverity-0:1.6- 100% |  28.8 MiB/s |  29.5 KiB |  00m00s
[ 83/171] Installing libevent-0:2.1.12- 100% | 216.5 MiB/s | 886.8 KiB |  00m00s
[ 84/171] Installing zstd-0:1.5.7-2.fc4 100% |  90.0 MiB/s |   1.7 MiB |  00m00s
[ 85/171] Installing util-linux-core-0: 100% |  61.7 MiB/s |   1.5 MiB |  00m00s
[ 86/171] Installing tar-2:1.35-6.fc43. 100% | 113.8 MiB/s |   3.0 MiB |  00m00s
[ 87/171] Installing libsemanage-0:3.9- 100% | 151.5 MiB/s | 310.2 KiB |  00m00s
[ 88/171] Installing systemd-standalone 100% |  19.4 MiB/s | 277.8 KiB |  00m00s
[ 89/171] Installing rpm-libs-0:5.99.91 100% | 182.7 MiB/s | 935.3 KiB |  00m00s
[ 90/171] Installing libusb1-0:1.0.28-2 100% |   7.3 MiB/s | 172.7 KiB |  00m00s
>>> Running sysusers scriptlet: tpm2-tss-0:4.1.3-8.fc43.x86_64                  
>>> Finished sysusers scriptlet: tpm2-tss-0:4.1.3-8.fc43.x86_64                 
>>> Scriptlet output:                                                           
>>> Creating group 'tss' with GID 59.                                           
>>> Creating user 'tss' (Account used for TPM access) with UID 59 and GID 59.   
>>>                                                                             
[ 91/171] Installing tpm2-tss-0:4.1.3-8 100% | 157.2 MiB/s |   1.6 MiB |  00m00s
[ 92/171] Installing ima-evm-utils-libs 100% |  60.5 MiB/s |  62.0 KiB |  00m00s
[ 93/171] Installing gnupg2-gpg-agent-0 100% |  18.3 MiB/s | 675.4 KiB |  00m00s
[ 94/171] Installing zip-0:3.0-44.fc43. 100% |  37.9 MiB/s | 698.4 KiB |  00m00s
[ 95/171] Installing gnupg2-keyboxd-0:2 100% |  10.4 MiB/s | 202.7 KiB |  00m00s
[ 96/171] Installing libpsl-0:0.21.5-6. 100% |  75.7 MiB/s |  77.5 KiB |  00m00s
[ 97/171] Installing liblastlog2-0:2.41 100% |   1.7 MiB/s |  35.8 KiB |  00m00s
[ 98/171] Installing libfdisk-0:2.41.1- 100% | 124.2 MiB/s | 381.6 KiB |  00m00s
[ 99/171] Installing nettle-0:3.10.1-2. 100% | 155.0 MiB/s | 793.7 KiB |  00m00s
[100/171] Installing gnutls-0:3.8.10-3. 100% | 225.8 MiB/s |   3.8 MiB |  00m00s
[101/171] Installing libxml2-0:2.12.10- 100% |  77.5 MiB/s |   1.7 MiB |  00m00s
[102/171] Installing libarchive-0:3.8.1 100% | 186.1 MiB/s | 953.1 KiB |  00m00s
[103/171] Installing bzip2-0:1.0.8-21.f 100% |   7.0 MiB/s |  99.8 KiB |  00m00s
[104/171] Installing add-determinism-0: 100% | 106.2 MiB/s |   2.4 MiB |  00m00s
[105/171] Installing build-reproducibil 100% |   1.0 MiB/s |   1.0 KiB |  00m00s
[106/171] Installing cpio-0:2.15-6.fc43 100% |  57.9 MiB/s |   1.1 MiB |  00m00s
[107/171] Installing diffutils-0:3.12-3 100% |  71.0 MiB/s |   1.6 MiB |  00m00s
[108/171] Installing libpkgconf-0:2.3.0 100% |  38.7 MiB/s |  79.2 KiB |  00m00s
[109/171] Installing pkgconf-0:2.3.0-3. 100% |   5.9 MiB/s |  91.0 KiB |  00m00s
[110/171] Installing ed-0:1.22-2.fc43.x 100% |  10.4 MiB/s | 149.4 KiB |  00m00s
[111/171] Installing patch-0:2.8-2.fc43 100% |  15.6 MiB/s | 224.3 KiB |  00m00s
[112/171] Installing jansson-0:2.14-3.f 100% |  44.2 MiB/s |  90.5 KiB |  00m00s
[113/171] Installing libgomp-0:15.1.1-5 100% | 176.0 MiB/s | 540.5 KiB |  00m00s
[114/171] Installing libtool-ltdl-0:2.5 100% |  69.6 MiB/s |  71.2 KiB |  00m00s
[115/171] Installing gdbm-libs-1:1.23-1 100% |  64.2 MiB/s | 131.6 KiB |  00m00s
[116/171] Installing cyrus-sasl-lib-0:2 100% |  99.8 MiB/s |   2.3 MiB |  00m00s
[117/171] Installing openldap-0:2.6.10- 100% | 128.8 MiB/s | 659.6 KiB |  00m00s
[118/171] Installing gnupg2-dirmngr-0:2 100% |  17.3 MiB/s | 621.1 KiB |  00m00s
[119/171] Installing gnupg2-0:2.4.8-4.f 100% | 139.4 MiB/s |   6.6 MiB |  00m00s
[120/171] Installing rpm-sign-libs-0:5. 100% |  13.2 MiB/s |  40.6 KiB |  00m00s
[121/171] Installing gpgverify-0:2.2-3. 100% |   9.2 MiB/s |   9.4 KiB |  00m00s
[122/171] Installing xxhash-libs-0:0.8. 100% |  89.4 MiB/s |  91.6 KiB |  00m00s
[123/171] Installing libbrotli-0:1.1.0- 100% | 204.0 MiB/s | 835.6 KiB |  00m00s
[124/171] Installing libnghttp2-0:1.66. 100% | 159.5 MiB/s | 163.3 KiB |  00m00s
[125/171] Installing keyutils-libs-0:1. 100% |  18.1 MiB/s |  55.7 KiB |  00m00s
[126/171] Installing libcom_err-0:1.47. 100% |  62.7 MiB/s |  64.2 KiB |  00m00s
[127/171] Installing libverto-0:0.3.2-1 100% |  26.6 MiB/s |  27.2 KiB |  00m00s
[128/171] Installing krb5-libs-0:1.21.3 100% | 163.7 MiB/s |   2.3 MiB |  00m00s
[129/171] Installing libssh-0:0.11.2-1. 100% | 111.1 MiB/s | 568.8 KiB |  00m00s
[130/171] Installing libcurl-0:8.15.0-2 100% | 220.8 MiB/s | 904.3 KiB |  00m00s
[131/171] Installing curl-0:8.15.0-2.fc 100% |  11.3 MiB/s | 476.3 KiB |  00m00s
[132/171] Installing rpm-0:5.99.91-2.fc 100% |  41.5 MiB/s |   2.5 MiB |  00m00s
[133/171] Installing efi-srpm-macros-0: 100% |  40.2 MiB/s |  41.1 KiB |  00m00s
[134/171] Installing java-srpm-macros-0 100% |   0.0   B/s |   1.1 KiB |  00m00s
[135/171] Installing lua-srpm-macros-0: 100% |   1.9 MiB/s |   1.9 KiB |  00m00s
[136/171] Installing tree-sitter-srpm-m 100% |   9.1 MiB/s |   9.3 KiB |  00m00s
[137/171] Installing zig-srpm-macros-0: 100% |   0.0   B/s |   1.7 KiB |  00m00s
[138/171] Installing filesystem-srpm-ma 100% |  12.7 MiB/s |  38.9 KiB |  00m00s
[139/171] Installing elfutils-default-y 100% | 120.2 KiB/s |   2.0 KiB |  00m00s
[140/171] Installing elfutils-libs-0:0. 100% | 167.3 MiB/s | 685.2 KiB |  00m00s
[141/171] Installing elfutils-debuginfo 100% |   5.3 MiB/s |  86.2 KiB |  00m00s
[142/171] Installing binutils-0:2.45-1. 100% | 174.6 MiB/s |  26.5 MiB |  00m00s
[143/171] Installing elfutils-0:0.193-3 100% |  91.2 MiB/s |   2.9 MiB |  00m00s
[144/171] Installing gdb-minimal-0:16.3 100% | 184.0 MiB/s |  13.2 MiB |  00m00s
[145/171] Installing debugedit-0:5.2-2. 100% |  11.5 MiB/s | 200.5 KiB |  00m00s
[146/171] Installing rpm-build-libs-0:5 100% | 131.5 MiB/s | 269.2 KiB |  00m00s
[147/171] Installing pkgconf-m4-0:2.3.0 100% |   0.0   B/s |  14.8 KiB |  00m00s
[148/171] Installing pkgconf-pkg-config 100% | 136.4 KiB/s |   1.8 KiB |  00m00s
[149/171] Installing rust-srpm-macros-0 100% |   5.4 MiB/s |   5.6 KiB |  00m00s
[150/171] Installing qt6-srpm-macros-0: 100% |   0.0   B/s | 740.0   B |  00m00s
[151/171] Installing qt5-srpm-macros-0: 100% |   0.0   B/s | 776.0   B |  00m00s
[152/171] Installing perl-srpm-macros-0 100% |   0.0   B/s |   1.1 KiB |  00m00s
[153/171] Installing package-notes-srpm 100% |   0.0   B/s |   2.0 KiB |  00m00s
[154/171] Installing openblas-srpm-macr 100% |   0.0   B/s | 392.0   B |  00m00s
[155/171] Installing ocaml-srpm-macros- 100% |   0.0   B/s |   2.1 KiB |  00m00s
[156/171] Installing kernel-srpm-macros 100% |   1.1 MiB/s |   2.3 KiB |  00m00s
[157/171] Installing gnat-srpm-macros-0 100% |   0.0   B/s |   1.3 KiB |  00m00s
[158/171] Installing ghc-srpm-macros-0: 100% |   0.0   B/s |   1.0 KiB |  00m00s
[159/171] Installing fpc-srpm-macros-0: 100% |   0.0   B/s | 420.0   B |  00m00s
[160/171] Installing ansible-srpm-macro 100% |  35.4 MiB/s |  36.2 KiB |  00m00s
[161/171] Installing rpm-build-0:5.99.9 100% |  16.9 MiB/s | 294.3 KiB |  00m00s
[162/171] Installing pyproject-srpm-mac 100% |   2.4 MiB/s |   2.5 KiB |  00m00s
[163/171] Installing redhat-rpm-config- 100% |  37.0 MiB/s | 189.3 KiB |  00m00s
[164/171] Installing forge-srpm-macros- 100% |  39.3 MiB/s |  40.3 KiB |  00m00s
[165/171] Installing fonts-srpm-macros- 100% |  55.7 MiB/s |  57.0 KiB |  00m00s
[166/171] Installing go-srpm-macros-0:3 100% |  61.3 MiB/s |  62.7 KiB |  00m00s
[167/171] Installing python-srpm-macros 100% |  25.8 MiB/s |  52.8 KiB |  00m00s
[168/171] Installing util-linux-0:2.41. 100% |  51.0 MiB/s |   3.6 MiB |  00m00s
[169/171] Installing shadow-utils-2:4.1 100% |  73.5 MiB/s |   4.0 MiB |  00m00s
[170/171] Installing which-0:2.23-3.fc4 100% |   5.6 MiB/s |  85.7 KiB |  00m00s
[171/171] Installing info-0:7.2-4.fc43. 100% | 119.3 KiB/s | 354.3 KiB |  00m03s
Warning: skipped OpenPGP checks for 169 packages from repository: http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch
Complete!
Finish: installing minimal buildroot with dnf5
Start: creating root cache
Finish: creating root cache
Finish: chroot init
INFO: Installed packages:
INFO: add-determinism-0.6.0-2.fc43.x86_64
alternatives-1.33-2.fc43.x86_64
ansible-srpm-macros-1-18.1.fc43.noarch
audit-libs-4.1.0-3.fc43.x86_64
bash-5.3.0-2.fc43.x86_64
binutils-2.45-1.fc43.x86_64
build-reproducibility-srpm-macros-0.6.0-2.fc43.noarch
bzip2-1.0.8-21.fc43.x86_64
bzip2-libs-1.0.8-21.fc43.x86_64
ca-certificates-2024.2.69_v8.0.401-7.fc43.noarch
coreutils-9.7-5.fc43.x86_64
coreutils-common-9.7-5.fc43.x86_64
cpio-2.15-6.fc43.x86_64
crypto-policies-20250714-3.gitcd6043a.fc43.noarch
curl-8.15.0-2.fc43.x86_64
cyrus-sasl-lib-2.1.28-33.fc43.x86_64
debugedit-5.2-2.fc43.x86_64
diffutils-3.12-3.fc43.x86_64
dwz-0.16-2.fc43.x86_64
ed-1.22-2.fc43.x86_64
efi-srpm-macros-6-4.fc43.noarch
elfutils-0.193-3.fc43.x86_64
elfutils-debuginfod-client-0.193-3.fc43.x86_64
elfutils-default-yama-scope-0.193-3.fc43.noarch
elfutils-libelf-0.193-3.fc43.x86_64
elfutils-libs-0.193-3.fc43.x86_64
fedora-gpg-keys-43-0.2.noarch
fedora-release-43-0.19.noarch
fedora-release-common-43-0.19.noarch
fedora-release-identity-basic-43-0.19.noarch
fedora-repos-43-0.2.noarch
fedora-repos-rawhide-43-0.2.noarch
file-5.46-6.fc43.x86_64
file-libs-5.46-6.fc43.x86_64
filesystem-3.18-45.fc43.x86_64
filesystem-srpm-macros-3.18-45.fc43.noarch
findutils-4.10.0-6.fc43.x86_64
fonts-srpm-macros-2.0.5-23.fc43.noarch
forge-srpm-macros-0.4.0-3.fc43.noarch
fpc-srpm-macros-1.3-15.fc43.noarch
gawk-5.3.2-2.fc43.x86_64
gdb-minimal-16.3-4.fc43.x86_64
gdbm-libs-1.23-10.fc43.x86_64
ghc-srpm-macros-1.9.2-3.fc43.noarch
glibc-2.41.9000-24.fc43.x86_64
glibc-common-2.41.9000-24.fc43.x86_64
glibc-gconv-extra-2.41.9000-24.fc43.x86_64
glibc-minimal-langpack-2.41.9000-24.fc43.x86_64
gmp-6.3.0-4.fc43.x86_64
gnat-srpm-macros-6-8.fc43.noarch
gnupg2-2.4.8-4.fc43.x86_64
gnupg2-dirmngr-2.4.8-4.fc43.x86_64
gnupg2-gpg-agent-2.4.8-4.fc43.x86_64
gnupg2-gpgconf-2.4.8-4.fc43.x86_64
gnupg2-keyboxd-2.4.8-4.fc43.x86_64
gnupg2-verify-2.4.8-4.fc43.x86_64
gnutls-3.8.10-3.fc43.x86_64
go-srpm-macros-3.7.0-4.fc43.noarch
gpgverify-2.2-3.fc43.noarch
grep-3.12-2.fc43.x86_64
gzip-1.13-4.fc43.x86_64
ima-evm-utils-libs-1.6.2-6.fc43.x86_64
info-7.2-4.fc43.x86_64
jansson-2.14-3.fc43.x86_64
java-srpm-macros-1-7.fc43.noarch
json-c-0.18-7.fc43.x86_64
kernel-srpm-macros-1.0-27.fc43.noarch
keyutils-libs-1.6.3-6.fc43.x86_64
krb5-libs-1.21.3-7.fc43.x86_64
libacl-2.3.2-4.fc43.x86_64
libarchive-3.8.1-3.fc43.x86_64
libassuan-2.5.7-4.fc43.x86_64
libattr-2.5.2-6.fc43.x86_64
libblkid-2.41.1-13.fc43.x86_64
libbrotli-1.1.0-8.fc43.x86_64
libcap-2.76-2.fc43.x86_64
libcap-ng-0.8.5-6.fc43.x86_64
libcom_err-1.47.3-2.fc43.x86_64
libcurl-8.15.0-2.fc43.x86_64
libeconf-0.7.9-2.fc43.x86_64
libevent-2.1.12-16.fc43.x86_64
libfdisk-2.41.1-13.fc43.x86_64
libffi-3.5.1-2.fc43.x86_64
libfsverity-1.6-3.fc43.x86_64
libgcc-15.1.1-5.fc43.1.x86_64
libgcrypt-1.11.1-2.fc43.x86_64
libgomp-15.1.1-5.fc43.1.x86_64
libgpg-error-1.55-2.fc43.x86_64
libidn2-2.3.8-2.fc43.x86_64
libksba-1.6.7-4.fc43.x86_64
liblastlog2-2.41.1-13.fc43.x86_64
libmount-2.41.1-13.fc43.x86_64
libnghttp2-1.66.0-2.fc43.x86_64
libpkgconf-2.3.0-3.fc43.x86_64
libpsl-0.21.5-6.fc43.x86_64
libselinux-3.9-2.fc43.x86_64
libsemanage-3.9-2.fc43.x86_64
libsepol-3.9-2.fc43.x86_64
libsmartcols-2.41.1-13.fc43.x86_64
libssh-0.11.2-1.fc43.x86_64
libssh-config-0.11.2-1.fc43.noarch
libstdc++-15.1.1-5.fc43.1.x86_64
libtasn1-4.20.0-2.fc43.x86_64
libtool-ltdl-2.5.4-7.fc43.x86_64
libunistring-1.1-10.fc43.x86_64
libusb1-1.0.28-2.fc43.x86_64
libuuid-2.41.1-13.fc43.x86_64
libverto-0.3.2-11.fc43.x86_64
libxcrypt-4.4.38-8.fc43.x86_64
libxml2-2.12.10-3.fc43.x86_64
libzstd-1.5.7-2.fc43.x86_64
lua-libs-5.4.8-2.fc43.x86_64
lua-srpm-macros-1-16.fc43.noarch
lz4-libs-1.10.0-3.fc43.x86_64
mpfr-4.2.2-2.fc43.x86_64
ncurses-base-6.5-7.20250614.fc43.noarch
ncurses-libs-6.5-7.20250614.fc43.x86_64
nettle-3.10.1-2.fc43.x86_64
npth-1.8-3.fc43.x86_64
ocaml-srpm-macros-11-2.fc43.noarch
openblas-srpm-macros-2-20.fc43.noarch
openldap-2.6.10-3.fc43.x86_64
openssl-libs-3.5.1-2.fc43.x86_64
p11-kit-0.25.5-9.fc43.x86_64
p11-kit-trust-0.25.5-9.fc43.x86_64
package-notes-srpm-macros-0.5-14.fc43.noarch
pam-libs-1.7.1-3.fc43.x86_64
patch-2.8-2.fc43.x86_64
pcre2-10.45-1.fc43.1.x86_64
pcre2-syntax-10.45-1.fc43.1.noarch
perl-srpm-macros-1-60.fc43.noarch
pkgconf-2.3.0-3.fc43.x86_64
pkgconf-m4-2.3.0-3.fc43.noarch
pkgconf-pkg-config-2.3.0-3.fc43.x86_64
popt-1.19-9.fc43.x86_64
publicsuffix-list-dafsa-20250616-2.fc43.noarch
pyproject-srpm-macros-1.18.3-2.fc43.noarch
python-srpm-macros-3.14-4.fc43.noarch
qt5-srpm-macros-5.15.17-2.fc43.noarch
qt6-srpm-macros-6.9.1-2.fc43.noarch
readline-8.3-2.fc43.x86_64
redhat-rpm-config-343-10.fc43.noarch
rpm-5.99.91-2.fc43.x86_64
rpm-build-5.99.91-2.fc43.x86_64
rpm-build-libs-5.99.91-2.fc43.x86_64
rpm-libs-5.99.91-2.fc43.x86_64
rpm-sequoia-1.9.0-2.fc43.x86_64
rpm-sign-libs-5.99.91-2.fc43.x86_64
rust-srpm-macros-26.3-5.fc43.noarch
sed-4.9-5.fc43.x86_64
setup-2.15.0-26.fc43.noarch
shadow-utils-4.18.0-3.fc43.x86_64
sqlite-libs-3.50.2-2.fc43.x86_64
systemd-libs-257.7-1.fc43.x86_64
systemd-standalone-sysusers-257.7-1.fc43.x86_64
tar-1.35-6.fc43.x86_64
tpm2-tss-4.1.3-8.fc43.x86_64
tree-sitter-srpm-macros-0.4.2-1.fc43.noarch
unzip-6.0-67.fc43.x86_64
util-linux-2.41.1-13.fc43.x86_64
util-linux-core-2.41.1-13.fc43.x86_64
which-2.23-3.fc43.x86_64
xxhash-libs-0.8.3-3.fc43.x86_64
xz-5.8.1-2.fc43.x86_64
xz-libs-5.8.1-2.fc43.x86_64
zig-srpm-macros-1-5.fc43.noarch
zip-3.0-44.fc43.x86_64
zlib-ng-compat-2.2.4-3.fc43.x86_64
zstd-1.5.7-2.fc43.x86_64
Start: buildsrpm
Start: rpmbuild -bs
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1753833600
Wrote: /builddir/build/SRPMS/python-dask-2025.4.1-3.fc43~bootstrap.src.rpm
Finish: rpmbuild -bs
INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan
INFO: /var/lib/mock/fedora-rawhide-x86_64-1753885010.838369/root/var/log/dnf5.log
INFO: chroot_scan: creating tarball /var/lib/copr-rpmbuild/results/chroot_scan.tar.gz
/bin/tar: Removing leading `/' from member names
Finish: buildsrpm
INFO: Done(/var/lib/copr-rpmbuild/workspace/workdir-hmpa4x_y/python-dask/python-dask.spec) Config(child) 0 minutes 39 seconds
INFO: Results and/or logs in: /var/lib/copr-rpmbuild/results
INFO: Cleaning up build root ('cleanup_on_success=True')
Start: clean chroot
INFO: unmounting tmpfs.
Finish: clean chroot
INFO: Start(/var/lib/copr-rpmbuild/results/python-dask-2025.4.1-3.fc43~bootstrap.src.rpm)  Config(fedora-rawhide-x86_64)
Start(bootstrap): chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-rawhide-x86_64-bootstrap-1753885010.838369/root.
INFO: reusing tmpfs at /var/lib/mock/fedora-rawhide-x86_64-bootstrap-1753885010.838369/root.
INFO: calling preinit hooks
INFO: enabled root cache
INFO: enabled package manager cache
Start(bootstrap): cleaning package manager metadata
Finish(bootstrap): cleaning package manager metadata
Finish(bootstrap): chroot init
Start: chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-rawhide-x86_64-1753885010.838369/root.
INFO: calling preinit hooks
INFO: enabled root cache
Start: unpacking root cache
Finish: unpacking root cache
INFO: enabled package manager cache
Start: cleaning package manager metadata
Finish: cleaning package manager metadata
INFO: enabled HW Info plugin
INFO: Buildroot is handled by package management downloaded with a bootstrap image:
  rpm-5.99.91-2.fc43.x86_64
  rpm-sequoia-1.9.0-2.fc43.x86_64
  dnf5-5.2.15.0-3.fc43.x86_64
  dnf5-plugins-5.2.15.0-3.fc43.x86_64
Finish: chroot init
Start: build phase for python-dask-2025.4.1-3.fc43~bootstrap.src.rpm
Start: build setup for python-dask-2025.4.1-3.fc43~bootstrap.src.rpm
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1753833600
Wrote: /builddir/build/SRPMS/python-dask-2025.4.1-3.fc43~bootstrap.src.rpm
Updating and loading repositories:
 Additional repo http_kojipkgs_fedorapr 100% |  38.7 KiB/s |   3.8 KiB |  00m00s
 Copr repository                        100% |  15.4 KiB/s |   1.5 KiB |  00m00s
 fedora                                 100% | 199.3 KiB/s |  25.9 KiB |  00m00s
Repositories loaded.
Package                               Arch   Version                        Repository                                                         Size
Installing:
 python3-Bottleneck                   x86_64 1.4.2-4.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 634.0 KiB
 python3-aiohttp                      x86_64 3.11.18-3.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.8 MiB
 python3-crick                        x86_64 0.0.8-6.fc43                   copr_base                                                     629.2 KiB
 python3-devel                        x86_64 3.14.0~rc1-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.9 MiB
 python3-fastavro                     x86_64 1.11.1-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.8 MiB
 python3-graphviz                     noarch 1:0.20.1-13.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 341.6 KiB
 python3-h5py                         x86_64 3.13.0-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   4.6 MiB
 python3-ipython                      noarch 8.37.0-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   4.7 MiB
 python3-jsonschema                   noarch 4.23.0-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 906.7 KiB
 python3-matplotlib                   x86_64 3.10.3-7.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  31.6 MiB
 python3-psutil                       x86_64 7.0.0-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.4 MiB
 python3-requests                     noarch 2.32.4-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 478.2 KiB
 python3-snappy                       noarch 0.7.3-2.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  61.7 KiB
 python3-sqlalchemy                   x86_64 2.0.42-1.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  24.2 MiB
 python3-tables                       x86_64 3.10.1-7.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  10.4 MiB
 python3-zarr                         noarch 2.18.7-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.3 MiB
Installing dependencies:
 abattis-cantarell-vf-fonts           noarch 0.301-15.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 192.7 KiB
 adobe-mappings-cmap                  noarch 20231115-3.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  15.2 MiB
 adobe-mappings-cmap-deprecated       noarch 20231115-3.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 582.1 KiB
 adobe-mappings-pdf                   noarch 20190401-10.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   4.4 MiB
 avahi-libs                           x86_64 0.9~rc2-3.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 171.6 KiB
 blosc                                x86_64 1.21.6-7.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 126.2 KiB
 blosc2                               x86_64 2.19.0-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 556.8 KiB
 cairo                                x86_64 1.18.4-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.8 MiB
 cairo-gobject                        x86_64 1.18.4-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  31.1 KiB
 cpuinfo                              x86_64 24.09.26-2.git1e83a2f.fc43.2   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 107.0 KiB
 cups-filesystem                      noarch 1:2.4.12-4.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   0.0   B
 cups-libs                            x86_64 1:2.4.12-4.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 618.7 KiB
 dbus-libs                            x86_64 1:1.16.0-4.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 345.5 KiB
 default-fonts-core-sans              noarch 4.2-5.fc43                     http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  11.9 KiB
 dejavu-sans-fonts                    noarch 2.37-26.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   5.5 MiB
 expat                                x86_64 2.7.1-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 294.2 KiB
 flexiblas                            x86_64 3.4.5-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  50.4 KiB
 flexiblas-netlib                     x86_64 3.4.5-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  10.9 MiB
 flexiblas-openblas-openmp            x86_64 3.4.5-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  39.2 KiB
 fontconfig                           x86_64 2.17.0-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 765.9 KiB
 fonts-filesystem                     noarch 1:2.0.5-23.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   0.0   B
 freetype                             x86_64 2.13.3-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 850.2 KiB
 fribidi                              x86_64 1.0.16-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 190.3 KiB
 gd                                   x86_64 2.3.3-19.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 403.6 KiB
 gdk-pixbuf2                          x86_64 2.42.12-10.fc42                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.5 MiB
 glib2                                x86_64 2.85.2-1.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  14.7 MiB
 google-droid-sans-fonts              noarch 20200215-23.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   6.3 MiB
 google-noto-fonts-common             noarch 20250701-4.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  17.7 KiB
 google-noto-sans-vf-fonts            noarch 20250701-4.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.4 MiB
 gpgme                                x86_64 1.24.3-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 587.9 KiB
 gpgmepp                              x86_64 1.24.3-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 429.3 KiB
 graphite2                            x86_64 1.3.14-19.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 191.8 KiB
 graphviz                             x86_64 13.1.1-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  20.8 MiB
 graphviz-libs                        x86_64 13.1.1-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.2 MiB
 gts                                  x86_64 0.7.6-51.20121130.fc43         http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 636.6 KiB
 harfbuzz                             x86_64 11.3.3-1.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.8 MiB
 hdf5                                 x86_64 1.14.6-6.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   6.4 MiB
 jbig2dec-libs                        x86_64 0.20-7.fc43                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 164.9 KiB
 jbigkit-libs                         x86_64 2.1-32.fc43                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 113.5 KiB
 lasi                                 x86_64 1.1.3-17.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 126.7 KiB
 lcms2                                x86_64 2.16-6.fc43                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 433.7 KiB
 libICE                               x86_64 1.1.2-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 194.4 KiB
 libSM                                x86_64 1.2.5-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 101.0 KiB
 libX11                               x86_64 1.8.12-1.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.3 MiB
 libX11-common                        noarch 1.8.12-1.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.2 MiB
 libXau                               x86_64 1.0.12-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  72.9 KiB
 libXext                              x86_64 1.3.6-4.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  90.0 KiB
 libXft                               x86_64 2.3.8-9.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 164.4 KiB
 libXpm                               x86_64 3.5.17-6.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 148.3 KiB
 libXrender                           x86_64 0.9.12-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  46.0 KiB
 libXt                                x86_64 1.3.1-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 429.8 KiB
 libaec                               x86_64 1.1.4-2.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  90.4 KiB
 libaom                               x86_64 3.12.0-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   5.0 MiB
 libavif                              x86_64 1.1.1-4.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 214.0 KiB
 libdatrie                            x86_64 0.2.13-12.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  53.8 KiB
 libdav1d                             x86_64 1.5.1-2.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.7 MiB
 libgfortran                          x86_64 15.1.1-5.fc43.1                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.4 MiB
 libgs                                x86_64 10.05.1-5.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  23.5 MiB
 libijs                               x86_64 0.35-25.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  61.6 KiB
 libimagequant                        x86_64 4.0.3-8.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 690.9 KiB
 libjpeg-turbo                        x86_64 3.1.1-2.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 799.5 KiB
 liblerc                              x86_64 4.0.0-9.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 624.1 KiB
 liblzf                               x86_64 3.6-33.fc43                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  49.5 KiB
 libpaper                             x86_64 1:2.1.1-9.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  48.7 KiB
 libpng                               x86_64 2:1.6.50-2.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 237.7 KiB
 libqhull_r                           x86_64 1:8.0.2-7.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 483.3 KiB
 libquadmath                          x86_64 15.1.1-5.fc43.1                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 317.9 KiB
 libraqm                              x86_64 0.10.1-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  28.6 KiB
 librsvg2                             x86_64 2.60.0-1.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   5.0 MiB
 libthai                              x86_64 0.1.29-11.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 783.4 KiB
 libtiff                              x86_64 4.7.0-6.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 619.0 KiB
 libvmaf                              x86_64 3.0.0-4.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 827.0 KiB
 libwebp                              x86_64 1.6.0-2.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 956.6 KiB
 libxcb                               x86_64 1.17.0-6.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.1 MiB
 libyuv                               x86_64 0-0.57.20240704git96bbdb5.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 679.7 KiB
 llhttp                               x86_64 9.3.0-5.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  92.6 KiB
 lzo                                  x86_64 2.10-15.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 178.9 KiB
 mpdecimal                            x86_64 4.0.1-2.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 217.2 KiB
 netpbm                               x86_64 11.10.00-2.fc43                http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 577.6 KiB
 nspr                                 x86_64 4.36.0-11.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 315.5 KiB
 nss                                  x86_64 3.113.0-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.9 MiB
 nss-softokn                          x86_64 3.113.0-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.9 MiB
 nss-softokn-freebl                   x86_64 3.113.0-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 848.4 KiB
 nss-sysinit                          x86_64 3.113.0-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  18.1 KiB
 nss-util                             x86_64 3.113.0-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 200.8 KiB
 openblas                             x86_64 0.3.29-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 111.7 KiB
 openblas-openmp                      x86_64 0.3.29-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  43.7 MiB
 openjpeg                             x86_64 2.5.3-7.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 451.7 KiB
 pango                                x86_64 1.56.4-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.0 MiB
 pixman                               x86_64 0.46.2-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 710.3 KiB
 poppler                              x86_64 25.07.0-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.8 MiB
 poppler-data                         noarch 0.4.11-10.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  12.3 MiB
 poppler-glib                         x86_64 25.07.0-2.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 633.9 KiB
 pyproject-rpm-macros                 noarch 1.18.3-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 114.5 KiB
 python-pip-wheel                     noarch 25.1.1-14.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.2 MiB
 python-rpm-macros                    noarch 3.14-4.fc43                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  22.8 KiB
 python3                              x86_64 3.14.0~rc1-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  28.9 KiB
 python3-aiohappyeyeballs             noarch 2.6.1-6.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 102.5 KiB
 python3-aiosignal                    noarch 1.4.0-2.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  24.8 KiB
 python3-asciitree                    noarch 0.3.3-33.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  20.9 KiB
 python3-asttokens                    noarch 3.0.0-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 199.2 KiB
 python3-attrs                        noarch 25.3.0-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 410.7 KiB
 python3-blosc2                       x86_64 3.2.0-2.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.8 MiB
 python3-cached_property              noarch 2.0.1-4.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  35.2 KiB
 python3-cairo                        x86_64 1.28.0-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 492.9 KiB
 python3-cffi                         x86_64 1.17.1-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.3 MiB
 python3-charset-normalizer           noarch 3.4.2-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 354.0 KiB
 python3-contourpy                    x86_64 1.3.3-1.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 856.7 KiB
 python3-cpuinfo                      noarch 9.0.0-14.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 310.8 KiB
 python3-cramjam                      x86_64 2.11.0-1.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.1 MiB
 python3-cycler                       noarch 0.11.0-17.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  37.8 KiB
 python3-dateutil                     noarch 1:2.9.0.post0-4.fc43           http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 877.6 KiB
 python3-decorator                    noarch 5.2.1-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  81.8 KiB
 python3-executing                    noarch 2.2.0-3.fc43~bootstrap         http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 275.9 KiB
 python3-fasteners                    noarch 0.19-15.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 117.2 KiB
 python3-fonttools                    x86_64 4.59.0-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  18.9 MiB
 python3-frozenlist                   x86_64 1.6.0-5.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 147.2 KiB
 python3-idna                         noarch 3.10-4.fc43                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 730.6 KiB
 python3-jedi                         noarch 0.19.2-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   5.7 MiB
 python3-jsonschema-specifications    noarch 2024.10.1-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  46.2 KiB
 python3-kiwisolver                   x86_64 1.4.8-4.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 154.0 KiB
 python3-libs                         x86_64 3.14.0~rc1-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  42.9 MiB
 python3-matplotlib-data-fonts        x86_64 3.10.3-7.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   7.9 MiB
 python3-matplotlib-inline            noarch 0.1.7-7.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  39.8 KiB
 python3-msgpack                      x86_64 1.1.0-4.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 335.6 KiB
 python3-multidict                    x86_64 6.6.3-2.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 323.3 KiB
 python3-ndindex                      x86_64 1.10.0-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.1 MiB
 python3-numcodecs                    x86_64 0.16.1-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.8 MiB
 python3-numexpr                      x86_64 2.10.2-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 795.0 KiB
 python3-numpy                        x86_64 1:2.3.2-1.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  41.5 MiB
 python3-numpy-f2py                   x86_64 1:2.3.2-1.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.1 MiB
 python3-olefile                      noarch 0.47-9.fc43                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 346.5 KiB
 python3-packaging                    noarch 25.0-4.fc43                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 607.5 KiB
 python3-parso                        noarch 0.8.4-8.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 929.3 KiB
 python3-pexpect                      noarch 4.9.0-12.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 624.7 KiB
 python3-pillow                       x86_64 11.3.0-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   4.3 MiB
 python3-platformdirs                 noarch 4.2.2-6.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 177.6 KiB
 python3-ply                          noarch 3.11-28.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 575.2 KiB
 python3-pooch                        noarch 1.8.2-7.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 635.3 KiB
 python3-prompt-toolkit               noarch 3.0.41-8.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   4.1 MiB
 python3-propcache                    x86_64 0.2.0-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 138.4 KiB
 python3-ptyprocess                   noarch 0.7.0-12.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  80.5 KiB
 python3-pure-eval                    noarch 0.2.3-4.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 106.0 KiB
 python3-pycparser                    noarch 2.22-3.fc43                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.5 MiB
 python3-pygments                     noarch 2.19.1-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  11.3 MiB
 python3-pyparsing                    noarch 3.1.2-12.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.0 MiB
 python3-referencing                  noarch 0.36.2-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 336.5 KiB
 python3-rpds-py                      x86_64 0.25.0-3.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 751.5 KiB
 python3-rpm-generators               noarch 14-13.fc43                     http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  81.7 KiB
 python3-rpm-macros                   noarch 3.14-4.fc43                    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   6.5 KiB
 python3-scipy                        x86_64 1.15.3-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  70.3 MiB
 python3-six                          noarch 1.17.0-5.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 118.0 KiB
 python3-stack-data                   noarch 0.6.3-16.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 223.3 KiB
 python3-traitlets                    noarch 5.14.3-6.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.0 MiB
 python3-typing-extensions            noarch 4.14.1-2.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 531.4 KiB
 python3-urllib3                      noarch 2.4.0-5.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.1 MiB
 python3-wcwidth                      noarch 0.2.13-14.fc43                 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 559.4 KiB
 python3-yarl                         x86_64 1.18.3-4.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 550.8 KiB
 rav1e-libs                           x86_64 0.7.1-7.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.0 MiB
 rsvg-pixbuf-loader                   x86_64 2.60.0-1.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 345.0 KiB
 shared-mime-info                     x86_64 2.4-2.fc43                     http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   5.2 MiB
 snappy                               x86_64 1.2.2-2.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  71.8 KiB
 svt-av1-libs                         x86_64 2.3.0-2.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   5.6 MiB
 tzdata                               noarch 2025b-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.6 MiB
 urw-base35-bookman-fonts             noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.4 MiB
 urw-base35-c059-fonts                noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.4 MiB
 urw-base35-d050000l-fonts            noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  84.3 KiB
 urw-base35-fonts                     noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   5.3 KiB
 urw-base35-fonts-common              noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  37.4 KiB
 urw-base35-gothic-fonts              noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.2 MiB
 urw-base35-nimbus-mono-ps-fonts      noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.0 MiB
 urw-base35-nimbus-roman-fonts        noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.4 MiB
 urw-base35-nimbus-sans-fonts         noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.4 MiB
 urw-base35-p052-fonts                noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.5 MiB
 urw-base35-standard-symbols-ps-fonts noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  64.9 KiB
 urw-base35-z003-fonts                noarch 20200910-26.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 390.8 KiB
 xml-common                           noarch 0.6.3-67.fc43                  http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  78.4 KiB
 zlib-ng                              x86_64 2.2.4-3.fc43                   http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 157.0 KiB

Transaction Summary:
 Installing:       194 packages

Total size of inbound packages is 135 MiB. Need to download 135 MiB.
After this operation, 577 MiB extra will be used (install 577 MiB, remove 0 B).
[  1/194] python3-Bottleneck-0:1.4.2-4. 100% |   1.0 MiB/s | 170.6 KiB |  00m00s
[  2/194] python3-devel-0:3.14.0~rc1-2. 100% |   2.1 MiB/s | 386.3 KiB |  00m00s
[  3/194] python3-aiohttp-0:3.11.18-3.f 100% |   3.7 MiB/s | 783.1 KiB |  00m00s
[  4/194] python3-crick-0:0.0.8-6.fc43. 100% |   2.4 MiB/s | 216.1 KiB |  00m00s
[  5/194] python3-graphviz-1:0.20.1-13. 100% |   2.2 MiB/s |  96.1 KiB |  00m00s
[  6/194] python3-fastavro-0:1.11.1-4.f 100% |   7.2 MiB/s | 589.5 KiB |  00m00s
[  7/194] python3-jsonschema-0:4.23.0-4 100% |   4.5 MiB/s | 201.9 KiB |  00m00s
[  8/194] python3-ipython-0:8.37.0-4.fc 100% |  16.9 MiB/s |   1.1 MiB |  00m00s
[  9/194] python3-h5py-0:3.13.0-3.fc43. 100% |  17.6 MiB/s |   1.2 MiB |  00m00s
[ 10/194] python3-snappy-0:0.7.3-2.fc43 100% | 525.8 KiB/s |  26.3 KiB |  00m00s
[ 11/194] python3-psutil-0:7.0.0-3.fc43 100% |   2.3 MiB/s | 254.3 KiB |  00m00s
[ 12/194] python3-matplotlib-0:3.10.3-7 100% |  51.1 MiB/s |   7.7 MiB |  00m00s
[ 13/194] python3-requests-0:2.32.4-3.f 100% |   1.6 MiB/s | 150.3 KiB |  00m00s
[ 14/194] python3-tables-0:3.10.1-7.fc4 100% |  22.8 MiB/s |   1.7 MiB |  00m00s
[ 15/194] python3-zarr-0:2.18.7-4.fc43. 100% |  10.5 MiB/s | 549.9 KiB |  00m00s
[ 16/194] python3-0:3.14.0~rc1-2.fc43.x 100% | 739.9 KiB/s |  27.4 KiB |  00m00s
[ 17/194] python3-asciitree-0:0.3.3-33. 100% | 420.3 KiB/s |  18.1 KiB |  00m00s
[ 18/194] python3-fasteners-0:0.19-15.f 100% |   1.1 MiB/s |  44.8 KiB |  00m00s
[ 19/194] python3-numcodecs-0:0.16.1-3. 100% |   9.4 MiB/s | 463.2 KiB |  00m00s
[ 20/194] blosc-0:1.21.6-7.fc43.x86_64  100% |   1.3 MiB/s |  58.1 KiB |  00m00s
[ 21/194] blosc2-0:2.19.0-2.fc43.x86_64 100% |   3.9 MiB/s | 218.8 KiB |  00m00s
[ 22/194] python3-numpy-1:2.3.2-1.fc43. 100% |  45.0 MiB/s |   8.1 MiB |  00m00s
[ 23/194] python3-sqlalchemy-0:2.0.42-1 100% |  12.1 MiB/s |   3.8 MiB |  00m00s
[ 24/194] hdf5-0:1.14.6-6.fc43.x86_64   100% |  21.1 MiB/s |   2.0 MiB |  00m00s
[ 25/194] lzo-0:2.10-15.fc43.x86_64     100% |   1.7 MiB/s |  69.4 KiB |  00m00s
[ 26/194] python3-cpuinfo-0:9.0.0-14.fc 100% |   1.7 MiB/s |  68.6 KiB |  00m00s
[ 27/194] python3-numexpr-0:2.10.2-4.fc 100% |   4.6 MiB/s | 208.3 KiB |  00m00s
[ 28/194] python3-blosc2-0:3.2.0-2.fc43 100% |   6.2 MiB/s | 509.1 KiB |  00m00s
[ 29/194] python3-packaging-0:25.0-4.fc 100% |   3.8 MiB/s | 151.0 KiB |  00m00s
[ 30/194] python3-six-0:1.17.0-5.fc43.n 100% |   1.1 MiB/s |  41.7 KiB |  00m00s
[ 31/194] python3-typing-extensions-0:4 100% |   2.8 MiB/s | 111.2 KiB |  00m00s
[ 32/194] snappy-0:1.2.2-2.fc43.x86_64  100% | 720.4 KiB/s |  39.6 KiB |  00m00s
[ 33/194] python3-charset-normalizer-0: 100% |   2.1 MiB/s | 108.6 KiB |  00m00s
[ 34/194] python3-cramjam-0:2.11.0-1.fc 100% |   7.5 MiB/s |   1.2 MiB |  00m00s
[ 35/194] python3-idna-0:3.10-4.fc43.no 100% |   2.5 MiB/s | 118.5 KiB |  00m00s
[ 36/194] python3-urllib3-0:2.4.0-5.fc4 100% |   6.1 MiB/s | 275.7 KiB |  00m00s
[ 37/194] python3-cffi-0:1.17.1-4.fc43. 100% |   1.3 MiB/s | 307.7 KiB |  00m00s
[ 38/194] python3-numpy-f2py-1:2.3.2-1. 100% |   9.2 MiB/s | 461.7 KiB |  00m00s
[ 39/194] dejavu-sans-fonts-0:2.37-26.f 100% |  17.4 MiB/s |   1.3 MiB |  00m00s
[ 40/194] libqhull_r-1:8.0.2-7.fc43.x86 100% |   4.3 MiB/s | 201.9 KiB |  00m00s
[ 41/194] python3-cairo-0:1.28.0-2.fc43 100% |   2.9 MiB/s | 126.1 KiB |  00m00s
[ 42/194] freetype-0:2.13.3-3.fc43.x86_ 100% |   2.7 MiB/s | 412.1 KiB |  00m00s
[ 43/194] python3-contourpy-0:1.3.3-1.f 100% |   6.3 MiB/s | 275.6 KiB |  00m00s
[ 44/194] python3-cycler-0:0.11.0-17.fc 100% | 493.1 KiB/s |  21.7 KiB |  00m00s
[ 45/194] python3-dateutil-1:2.9.0.post 100% |   7.4 MiB/s | 333.6 KiB |  00m00s
[ 46/194] flexiblas-netlib-0:3.4.5-3.fc 100% |   9.6 MiB/s |   3.4 MiB |  00m00s
[ 47/194] python3-kiwisolver-0:1.4.8-4. 100% |   1.5 MiB/s |  67.9 KiB |  00m00s
[ 48/194] python3-matplotlib-data-fonts 100% |  17.9 MiB/s |   2.3 MiB |  00m00s
[ 49/194] python3-pillow-0:11.3.0-3.fc4 100% |   6.2 MiB/s |   1.0 MiB |  00m00s
[ 50/194] python3-pyparsing-0:3.1.2-12. 100% |   5.7 MiB/s | 279.6 KiB |  00m00s
[ 51/194] python3-attrs-0:25.3.0-4.fc43 100% |   2.9 MiB/s | 122.7 KiB |  00m00s
[ 52/194] python3-fonttools-0:4.59.0-3. 100% |  10.3 MiB/s |   3.0 MiB |  00m00s
[ 53/194] python3-jsonschema-specificat 100% | 520.5 KiB/s |  21.9 KiB |  00m00s
[ 54/194] python3-referencing-0:0.36.2- 100% |   1.9 MiB/s |  80.4 KiB |  00m00s
[ 55/194] python3-decorator-0:5.2.1-3.f 100% | 795.7 KiB/s |  31.8 KiB |  00m00s
[ 56/194] python3-rpds-py-0:0.25.0-3.fc 100% |   4.9 MiB/s | 309.0 KiB |  00m00s
[ 57/194] python3-matplotlib-inline-0:0 100% | 587.0 KiB/s |  24.1 KiB |  00m00s
[ 58/194] python3-pexpect-0:4.9.0-12.fc 100% |   3.5 MiB/s | 161.9 KiB |  00m00s
[ 59/194] python3-jedi-0:0.19.2-3.fc43. 100% |  13.8 MiB/s |   1.3 MiB |  00m00s
[ 60/194] python3-stack-data-0:0.6.3-16 100% |   1.3 MiB/s |  60.9 KiB |  00m00s
[ 61/194] python3-prompt-toolkit-0:3.0. 100% |   5.9 MiB/s | 823.0 KiB |  00m00s
[ 62/194] python3-traitlets-0:5.14.3-6. 100% |   2.8 MiB/s | 211.7 KiB |  00m00s
[ 63/194] python3-pygments-0:2.19.1-4.f 100% |  16.0 MiB/s |   2.5 MiB |  00m00s
[ 64/194] liblzf-0:3.6-33.fc43.x86_64   100% | 578.8 KiB/s |  27.8 KiB |  00m00s
[ 65/194] python3-cached_property-0:2.0 100% | 471.6 KiB/s |  20.3 KiB |  00m00s
[ 66/194] tzdata-0:2025b-3.fc43.noarch  100% |   5.2 MiB/s | 429.3 KiB |  00m00s
[ 67/194] python3-pycparser-0:2.22-3.fc 100% |   4.0 MiB/s | 264.1 KiB |  00m00s
[ 68/194] graphviz-0:13.1.1-2.fc43.x86_ 100% |  20.6 MiB/s |   4.7 MiB |  00m00s
[ 69/194] python3-msgpack-0:1.1.0-4.fc4 100% |   2.6 MiB/s | 110.4 KiB |  00m00s
[ 70/194] cairo-0:1.18.4-2.fc43.x86_64  100% |   5.9 MiB/s | 729.3 KiB |  00m00s
[ 71/194] python3-ndindex-0:1.10.0-3.fc 100% |   5.5 MiB/s | 271.6 KiB |  00m00s
[ 72/194] python3-platformdirs-0:4.2.2- 100% |   1.1 MiB/s |  43.5 KiB |  00m00s
[ 73/194] llhttp-0:9.3.0-5.fc43.x86_64  100% | 970.2 KiB/s |  38.8 KiB |  00m00s
[ 74/194] python3-aiohappyeyeballs-0:2. 100% | 964.0 KiB/s |  38.6 KiB |  00m00s
[ 75/194] python3-aiosignal-0:1.4.0-2.f 100% | 469.1 KiB/s |  19.7 KiB |  00m00s
[ 76/194] python3-frozenlist-0:1.6.0-5. 100% |   1.4 MiB/s |  60.4 KiB |  00m00s
[ 77/194] python3-multidict-0:6.6.3-2.f 100% |   2.1 MiB/s |  86.6 KiB |  00m00s
[ 78/194] python3-propcache-0:0.2.0-3.f 100% |   1.4 MiB/s |  59.4 KiB |  00m00s
[ 79/194] python3-yarl-0:1.18.3-4.fc43. 100% |   3.3 MiB/s | 140.4 KiB |  00m00s
[ 80/194] libaec-0:1.1.4-2.fc43.x86_64  100% | 984.7 KiB/s |  37.4 KiB |  00m00s
[ 81/194] libgfortran-0:15.1.1-5.fc43.1 100% |  14.1 MiB/s | 968.7 KiB |  00m00s
[ 82/194] expat-0:2.7.1-3.fc43.x86_64   100% |   2.8 MiB/s | 116.1 KiB |  00m00s
[ 83/194] fontconfig-0:2.17.0-3.fc43.x8 100% |   6.2 MiB/s | 259.1 KiB |  00m00s
[ 84/194] gd-0:2.3.3-19.fc43.x86_64     100% |   3.2 MiB/s | 136.3 KiB |  00m00s
[ 85/194] gdk-pixbuf2-0:2.42.12-10.fc42 100% |  11.0 MiB/s | 471.5 KiB |  00m00s
[ 86/194] glib2-0:2.85.2-1.fc43.x86_64  100% |  22.3 MiB/s |   3.1 MiB |  00m00s
[ 87/194] graphviz-libs-0:13.1.1-2.fc43 100% |  10.9 MiB/s | 479.1 KiB |  00m00s
[ 88/194] gts-0:0.7.6-51.20121130.fc43. 100% |   5.0 MiB/s | 234.8 KiB |  00m00s
[ 89/194] python3-libs-0:3.14.0~rc1-2.f 100% |   9.3 MiB/s |   9.5 MiB |  00m01s
[ 90/194] lasi-0:1.1.3-17.fc43.x86_64   100% |   1.3 MiB/s |  55.0 KiB |  00m00s
[ 91/194] harfbuzz-0:11.3.3-1.fc43.x86_ 100% |  12.7 MiB/s |   1.1 MiB |  00m00s
[ 92/194] libX11-common-0:1.8.12-1.fc43 100% |   3.5 MiB/s | 144.9 KiB |  00m00s
[ 93/194] libX11-0:1.8.12-1.fc43.x86_64 100% |  10.7 MiB/s | 655.6 KiB |  00m00s
[ 94/194] libXrender-0:0.9.12-3.fc43.x8 100% | 707.2 KiB/s |  26.9 KiB |  00m00s
[ 95/194] librsvg2-0:2.60.0-1.fc43.x86_ 100% |  18.9 MiB/s |   1.9 MiB |  00m00s
[ 96/194] libwebp-0:1.6.0-2.fc43.x86_64 100% |   7.7 MiB/s | 324.2 KiB |  00m00s
[ 97/194] pango-0:1.56.4-2.fc43.x86_64  100% |   8.2 MiB/s | 350.7 KiB |  00m00s
[ 98/194] poppler-glib-0:25.07.0-2.fc43 100% |   5.1 MiB/s | 207.2 KiB |  00m00s
[ 99/194] urw-base35-fonts-0:20200910-2 100% | 247.0 KiB/s |   9.9 KiB |  00m00s
[100/194] libgs-0:10.05.1-5.fc43.x86_64 100% |  10.3 MiB/s |   3.4 MiB |  00m00s
[101/194] libjpeg-turbo-0:3.1.1-2.fc43. 100% |   5.8 MiB/s | 236.1 KiB |  00m00s
[102/194] libpng-2:1.6.50-2.fc43.x86_64 100% |   3.2 MiB/s | 123.0 KiB |  00m00s
[103/194] shared-mime-info-0:2.4-2.fc43 100% |   6.6 MiB/s | 399.7 KiB |  00m00s
[104/194] libXpm-0:3.5.17-6.fc43.x86_64 100% |   1.6 MiB/s |  65.6 KiB |  00m00s
[105/194] libavif-0:1.1.1-4.fc43.x86_64 100% |   2.4 MiB/s |  99.3 KiB |  00m00s
[106/194] libimagequant-0:4.0.3-8.fc43. 100% |   5.3 MiB/s | 313.8 KiB |  00m00s
[107/194] libtiff-0:4.7.0-6.fc43.x86_64 100% |   5.5 MiB/s | 220.8 KiB |  00m00s
[108/194] default-fonts-core-sans-0:4.2 100% | 660.8 KiB/s |  23.8 KiB |  00m00s
[109/194] fonts-filesystem-1:2.0.5-23.f 100% | 234.5 KiB/s |   8.7 KiB |  00m00s
[110/194] xml-common-0:0.6.3-67.fc43.no 100% | 862.4 KiB/s |  31.0 KiB |  00m00s
[111/194] flexiblas-0:3.4.5-3.fc43.x86_ 100% | 685.1 KiB/s |  26.0 KiB |  00m00s
[112/194] flexiblas-openblas-openmp-0:3 100% | 448.5 KiB/s |  17.0 KiB |  00m00s
[113/194] libquadmath-0:15.1.1-5.fc43.1 100% |   4.9 MiB/s | 200.9 KiB |  00m00s
[114/194] abattis-cantarell-vf-fonts-0: 100% |   3.1 MiB/s | 120.1 KiB |  00m00s
[115/194] libXext-0:1.3.6-4.fc43.x86_64 100% |   1.0 MiB/s |  39.1 KiB |  00m00s
[116/194] google-noto-sans-vf-fonts-0:2 100% |   7.9 MiB/s | 614.6 KiB |  00m00s
[117/194] libxcb-0:1.17.0-6.fc43.x86_64 100% |   5.7 MiB/s | 232.8 KiB |  00m00s
[118/194] pixman-0:0.46.2-2.fc43.x86_64 100% |   4.9 MiB/s | 292.5 KiB |  00m00s
[119/194] zlib-ng-0:2.2.4-3.fc43.x86_64 100% |   2.2 MiB/s |  86.6 KiB |  00m00s
[120/194] urw-base35-bookman-fonts-0:20 100% |  13.8 MiB/s | 845.2 KiB |  00m00s
[121/194] urw-base35-d050000l-fonts-0:2 100% |   1.6 MiB/s |  75.2 KiB |  00m00s
[122/194] urw-base35-c059-fonts-0:20200 100% |   8.1 MiB/s | 872.9 KiB |  00m00s
[123/194] urw-base35-fonts-common-0:202 100% | 404.5 KiB/s |  20.6 KiB |  00m00s
[124/194] urw-base35-gothic-fonts-0:202 100% |   6.8 MiB/s | 640.8 KiB |  00m00s
[125/194] urw-base35-nimbus-mono-ps-fon 100% |  13.1 MiB/s | 793.8 KiB |  00m00s
[126/194] urw-base35-nimbus-roman-fonts 100% |  13.9 MiB/s | 853.1 KiB |  00m00s
[127/194] urw-base35-p052-fonts-0:20200 100% |  15.3 MiB/s | 972.1 KiB |  00m00s
[128/194] urw-base35-nimbus-sans-fonts- 100% |   9.7 MiB/s |   1.3 MiB |  00m00s
[129/194] urw-base35-standard-symbols-p 100% |   1.5 MiB/s |  57.7 KiB |  00m00s
[130/194] urw-base35-z003-fonts-0:20200 100% |   6.4 MiB/s | 274.9 KiB |  00m00s
[131/194] python3-asttokens-0:3.0.0-3.f 100% |   1.4 MiB/s |  55.2 KiB |  00m00s
[132/194] python3-executing-0:2.2.0-3.f 100% |   1.6 MiB/s |  68.7 KiB |  00m00s
[133/194] python3-pure-eval-0:0.2.3-4.f 100% | 909.4 KiB/s |  35.5 KiB |  00m00s
[134/194] python3-pooch-0:1.8.2-7.fc43. 100% |   2.4 MiB/s | 126.9 KiB |  00m00s
[135/194] python3-ply-0:3.11-28.fc43.no 100% |   2.4 MiB/s | 132.1 KiB |  00m00s
[136/194] python3-wcwidth-0:0.2.13-14.f 100% |   1.2 MiB/s |  47.3 KiB |  00m00s
[137/194] lcms2-0:2.16-6.fc43.x86_64    100% |   4.6 MiB/s | 182.8 KiB |  00m00s
[138/194] libraqm-0:0.10.1-3.fc43.x86_6 100% | 476.9 KiB/s |  22.4 KiB |  00m00s
[139/194] openjpeg-0:2.5.3-7.fc43.x86_6 100% |   4.2 MiB/s | 191.7 KiB |  00m00s
[140/194] python3-ptyprocess-0:0.7.0-12 100% | 785.7 KiB/s |  36.1 KiB |  00m00s
[141/194] mpdecimal-0:4.0.1-2.fc43.x86_ 100% |   2.3 MiB/s |  97.1 KiB |  00m00s
[142/194] python-pip-wheel-0:25.1.1-14. 100% |  14.9 MiB/s |   1.2 MiB |  00m00s
[143/194] python3-parso-0:0.8.4-8.fc43. 100% |   4.6 MiB/s | 204.0 KiB |  00m00s
[144/194] python3-olefile-0:0.47-9.fc43 100% | 255.9 KiB/s |  74.7 KiB |  00m00s
[145/194] poppler-0:25.07.0-2.fc43.x86_ 100% |  15.7 MiB/s |   1.3 MiB |  00m00s
[146/194] gpgmepp-0:1.24.3-4.fc43.x86_6 100% |   3.5 MiB/s | 142.8 KiB |  00m00s
[147/194] nspr-0:4.36.0-11.fc43.x86_64  100% |   3.3 MiB/s | 137.2 KiB |  00m00s
[148/194] python3-scipy-0:1.15.3-4.fc43 100% |   7.0 MiB/s |  16.7 MiB |  00m02s
[149/194] nss-0:3.113.0-2.fc43.x86_64   100% |   9.9 MiB/s | 711.7 KiB |  00m00s
[150/194] fribidi-0:1.0.16-3.fc43.x86_6 100% |   1.3 MiB/s |  53.3 KiB |  00m00s
[151/194] libXft-0:2.3.8-9.fc43.x86_64  100% |   1.9 MiB/s |  72.2 KiB |  00m00s
[152/194] libthai-0:0.1.29-11.fc43.x86_ 100% |   5.1 MiB/s | 210.3 KiB |  00m00s
[153/194] nss-sysinit-0:3.113.0-2.fc43. 100% | 484.5 KiB/s |  18.9 KiB |  00m00s
[154/194] poppler-data-0:0.4.11-10.fc43 100% |  10.3 MiB/s |   1.9 MiB |  00m00s
[155/194] nss-util-0:3.113.0-2.fc43.x86 100% |   2.1 MiB/s |  85.6 KiB |  00m00s
[156/194] nss-softokn-0:3.113.0-2.fc43. 100% |   3.0 MiB/s | 416.4 KiB |  00m00s
[157/194] libXau-0:1.0.12-3.fc43.x86_64 100% | 857.6 KiB/s |  33.4 KiB |  00m00s
[158/194] jbigkit-libs-0:2.1-32.fc43.x8 100% |   1.4 MiB/s |  53.2 KiB |  00m00s
[159/194] liblerc-0:4.0.0-9.fc43.x86_64 100% |   5.4 MiB/s | 215.1 KiB |  00m00s
[160/194] libdatrie-0:0.2.13-12.fc43.x8 100% | 864.8 KiB/s |  32.0 KiB |  00m00s
[161/194] cairo-gobject-0:1.18.4-2.fc43 100% | 445.5 KiB/s |  16.9 KiB |  00m00s
[162/194] rsvg-pixbuf-loader-0:2.60.0-1 100% |   3.7 MiB/s | 167.8 KiB |  00m00s
[163/194] libdav1d-0:1.5.1-2.fc43.x86_6 100% |  10.8 MiB/s | 639.4 KiB |  00m00s
[164/194] adobe-mappings-cmap-deprecate 100% |   2.6 MiB/s | 102.5 KiB |  00m00s
[165/194] cups-libs-1:2.4.12-4.fc43.x86 100% |   6.1 MiB/s | 261.4 KiB |  00m00s
[166/194] adobe-mappings-pdf-0:20190401 100% |   7.6 MiB/s | 611.0 KiB |  00m00s
[167/194] jbig2dec-libs-0:0.20-7.fc43.x 100% |   1.8 MiB/s |  74.0 KiB |  00m00s
[168/194] libXt-0:1.3.1-3.fc43.x86_64   100% |   4.4 MiB/s | 179.2 KiB |  00m00s
[169/194] google-droid-sans-fonts-0:202 100% |  20.3 MiB/s |   2.7 MiB |  00m00s
[170/194] libijs-0:0.35-25.fc43.x86_64  100% | 693.2 KiB/s |  29.1 KiB |  00m00s
[171/194] libpaper-1:2.1.1-9.fc43.x86_6 100% | 699.8 KiB/s |  26.6 KiB |  00m00s
[172/194] libyuv-0:0-0.57.20240704git96 100% |   4.9 MiB/s | 202.1 KiB |  00m00s
[173/194] rav1e-libs-0:0.7.1-7.fc43.x86 100% |  16.2 MiB/s |   1.0 MiB |  00m00s
[174/194] libaom-0:3.12.0-3.fc43.x86_64 100% |  10.8 MiB/s |   1.8 MiB |  00m00s
[175/194] libvmaf-0:3.0.0-4.fc43.x86_64 100% |   4.7 MiB/s | 196.6 KiB |  00m00s
[176/194] adobe-mappings-cmap-0:2023111 100% |   4.4 MiB/s |   2.2 MiB |  00m00s
[177/194] svt-av1-libs-0:2.3.0-2.fc43.x 100% |  20.1 MiB/s |   2.0 MiB |  00m00s
[178/194] libICE-0:1.1.2-3.fc43.x86_64  100% |   2.0 MiB/s |  78.1 KiB |  00m00s
[179/194] libSM-0:1.2.5-3.fc43.x86_64   100% |   1.1 MiB/s |  44.2 KiB |  00m00s
[180/194] graphite2-0:1.3.14-19.fc43.x8 100% |   2.5 MiB/s |  95.6 KiB |  00m00s
[181/194] netpbm-0:11.10.00-2.fc43.x86_ 100% |   3.1 MiB/s | 187.0 KiB |  00m00s
[182/194] gpgme-0:1.24.3-4.fc43.x86_64  100% |   5.5 MiB/s | 217.9 KiB |  00m00s
[183/194] google-noto-fonts-common-0:20 100% | 471.7 KiB/s |  17.5 KiB |  00m00s
[184/194] avahi-libs-0:0.9~rc2-3.fc43.x 100% |   1.7 MiB/s |  68.9 KiB |  00m00s
[185/194] cups-filesystem-1:2.4.12-4.fc 100% | 349.1 KiB/s |  12.9 KiB |  00m00s
[186/194] dbus-libs-1:1.16.0-4.fc43.x86 100% |   3.6 MiB/s | 148.3 KiB |  00m00s
[187/194] cpuinfo-0:24.09.26-2.git1e83a 100% |   1.1 MiB/s |  44.0 KiB |  00m00s
[188/194] openblas-0:0.3.29-2.fc43.x86_ 100% |   1.0 MiB/s |  42.3 KiB |  00m00s
[189/194] pyproject-rpm-macros-0:1.18.3 100% |   1.1 MiB/s |  44.3 KiB |  00m00s
[190/194] nss-softokn-freebl-0:3.113.0- 100% |   3.5 MiB/s | 328.8 KiB |  00m00s
[191/194] python-rpm-macros-0:3.14-4.fc 100% | 452.2 KiB/s |  17.2 KiB |  00m00s
[192/194] openblas-openmp-0:0.3.29-2.fc 100% |  24.0 MiB/s |   5.4 MiB |  00m00s
[193/194] python3-rpm-generators-0:14-1 100% | 731.7 KiB/s |  28.5 KiB |  00m00s
[194/194] python3-rpm-macros-0:3.14-4.f 100% | 317.5 KiB/s |  11.7 KiB |  00m00s
--------------------------------------------------------------------------------
[194/194] Total                         100% |  23.4 MiB/s | 134.9 MiB |  00m06s
Running transaction
[  1/196] Verify package files          100% | 374.0   B/s | 194.0   B |  00m01s
[  2/196] Prepare transaction           100% | 910.0   B/s | 194.0   B |  00m00s
[  3/196] Installing glib2-0:2.85.2-1.f 100% | 177.6 MiB/s |  14.7 MiB |  00m00s
[  4/196] Installing libjpeg-turbo-0:3. 100% | 260.8 MiB/s | 801.2 KiB |  00m00s
[  5/196] Installing nspr-0:4.36.0-11.f 100% | 154.8 MiB/s | 317.1 KiB |  00m00s
[  6/196] Installing fonts-filesystem-1 100% | 769.5 KiB/s | 788.0   B |  00m00s
[  7/196] Installing urw-base35-fonts-c 100% |  37.5 MiB/s |  38.4 KiB |  00m00s
[  8/196] Installing libpng-2:1.6.50-2. 100% | 116.7 MiB/s | 239.0 KiB |  00m00s
[  9/196] Installing nss-util-0:3.113.0 100% | 197.0 MiB/s | 201.8 KiB |  00m00s
[ 10/196] Installing libwebp-0:1.6.0-2. 100% | 187.6 MiB/s | 960.7 KiB |  00m00s
[ 11/196] Installing libgfortran-0:15.1 100% | 305.0 MiB/s |   3.4 MiB |  00m00s
[ 12/196] Installing python-rpm-macros- 100% |  23.0 MiB/s |  23.5 KiB |  00m00s
[ 13/196] Installing openjpeg-0:2.5.3-7 100% | 110.8 MiB/s | 453.7 KiB |  00m00s
[ 14/196] Installing lcms2-0:2.16-6.fc4 100% | 212.6 MiB/s | 435.3 KiB |  00m00s
[ 15/196] Installing expat-0:2.7.1-3.fc 100% |  19.3 MiB/s | 296.3 KiB |  00m00s
[ 16/196] Installing python3-rpm-macros 100% |   0.0   B/s |   6.8 KiB |  00m00s
[ 17/196] Installing libICE-0:1.1.2-3.f 100% |  63.7 MiB/s | 195.8 KiB |  00m00s
[ 18/196] Installing adobe-mappings-cma 100% | 223.4 MiB/s |  15.2 MiB |  00m00s
[ 19/196] Installing libdav1d-0:1.5.1-2 100% | 188.2 MiB/s |   1.7 MiB |  00m00s
[ 20/196] Installing fribidi-0:1.0.16-3 100% |  11.8 MiB/s | 192.8 KiB |  00m00s
[ 21/196] Installing libimagequant-0:4. 100% | 112.7 MiB/s | 692.5 KiB |  00m00s
[ 22/196] Installing tzdata-0:2025b-3.f 100% |  24.6 MiB/s |   1.9 MiB |  00m00s
[ 23/196] Installing snappy-0:1.2.2-2.f 100% |  35.9 MiB/s |  73.5 KiB |  00m00s
[ 24/196] Installing blosc-0:1.21.6-7.f 100% |  63.0 MiB/s | 129.0 KiB |  00m00s
[ 25/196] Installing lzo-0:2.10-15.fc43 100% |  88.1 MiB/s | 180.5 KiB |  00m00s
[ 26/196] Installing adobe-mappings-cma 100% | 190.5 MiB/s | 585.2 KiB |  00m00s
[ 27/196] Installing libSM-0:1.2.5-3.fc 100% | 100.0 MiB/s | 102.4 KiB |  00m00s
[ 28/196] Installing pyproject-rpm-macr 100% | 113.7 MiB/s | 116.5 KiB |  00m00s
[ 29/196] Installing graphviz-libs-0:13 100% | 154.2 MiB/s |   1.2 MiB |  00m00s
[ 30/196] Installing nss-softokn-freebl 100% | 166.1 MiB/s | 850.5 KiB |  00m00s
[ 31/196] Installing nss-softokn-0:3.11 100% | 270.5 MiB/s |   1.9 MiB |  00m00s
[ 32/196] Installing nss-0:3.113.0-2.fc 100% |  94.0 MiB/s |   1.9 MiB |  00m00s
[ 33/196] Installing nss-sysinit-0:3.11 100% |   1.2 MiB/s |  19.2 KiB |  00m00s
[ 34/196] Installing urw-base35-bookman 100% | 273.0 MiB/s |   1.4 MiB |  00m00s
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[ 36/196] Installing urw-base35-d050000 100% |  83.4 MiB/s |  85.4 KiB |  00m00s
[ 37/196] Installing urw-base35-gothic- 100% | 290.7 MiB/s |   1.2 MiB |  00m00s
[ 38/196] Installing urw-base35-nimbus- 100% | 263.0 MiB/s |   1.1 MiB |  00m00s
[ 39/196] Installing urw-base35-nimbus- 100% | 273.2 MiB/s |   1.4 MiB |  00m00s
[ 40/196] Installing urw-base35-nimbus- 100% | 299.3 MiB/s |   2.4 MiB |  00m00s
[ 41/196] Installing urw-base35-p052-fo 100% | 297.5 MiB/s |   1.5 MiB |  00m00s
[ 42/196] Installing urw-base35-standar 100% |  64.4 MiB/s |  66.0 KiB |  00m00s
[ 43/196] Installing urw-base35-z003-fo 100% | 191.3 MiB/s | 391.8 KiB |  00m00s
[ 44/196] Installing urw-base35-fonts-0 100% |   0.0   B/s |   5.6 KiB |  00m00s
[ 45/196] Installing dejavu-sans-fonts- 100% | 289.9 MiB/s |   5.5 MiB |  00m00s
[ 46/196] Installing abattis-cantarell- 100% |  47.5 MiB/s | 194.4 KiB |  00m00s
[ 47/196] Installing google-droid-sans- 100% | 272.1 MiB/s |   6.3 MiB |  00m00s
[ 48/196] Installing libyuv-0:0-0.57.20 100% | 166.3 MiB/s | 681.3 KiB |  00m00s
[ 49/196] Installing shared-mime-info-0 100% |  64.7 MiB/s |   2.6 MiB |  00m00s
[ 50/196] Installing gdk-pixbuf2-0:2.42 100% |  72.2 MiB/s |   2.5 MiB |  00m00s
[ 51/196] Installing openblas-0:0.3.29- 100% |  36.9 MiB/s | 113.5 KiB |  00m00s
[ 52/196] Installing openblas-openmp-0: 100% | 408.3 MiB/s |  43.7 MiB |  00m00s
[ 53/196] Installing cpuinfo-0:24.09.26 100% |   6.7 MiB/s | 109.7 KiB |  00m00s
[ 54/196] Installing svt-av1-libs-0:2.3 100% | 280.6 MiB/s |   5.6 MiB |  00m00s
[ 55/196] Installing dbus-libs-1:1.16.0 100% | 169.3 MiB/s | 346.6 KiB |  00m00s
[ 56/196] Installing avahi-libs-0:0.9~r 100% |  85.0 MiB/s | 174.2 KiB |  00m00s
[ 57/196] Installing cups-filesystem-1: 100% |   1.7 MiB/s |   1.8 KiB |  00m00s
[ 58/196] Installing cups-libs-1:2.4.12 100% | 151.4 MiB/s | 620.3 KiB |  00m00s
[ 59/196] Installing google-noto-fonts- 100% |  18.1 MiB/s |  18.5 KiB |  00m00s
[ 60/196] Installing google-noto-sans-v 100% | 198.8 MiB/s |   1.4 MiB |  00m00s
[ 61/196] Installing default-fonts-core 100% |   8.9 MiB/s |  18.2 KiB |  00m00s
[ 62/196] Installing gpgme-0:1.24.3-4.f 100% |  33.9 MiB/s | 590.4 KiB |  00m00s
[ 63/196] Installing gpgmepp-0:1.24.3-4 100% |  84.1 MiB/s | 430.4 KiB |  00m00s
[ 64/196] Installing netpbm-0:11.10.00- 100% | 188.6 MiB/s | 579.5 KiB |  00m00s
[ 65/196] Installing gts-0:0.7.6-51.201 100% |  33.1 MiB/s | 643.7 KiB |  00m00s
[ 66/196] Installing graphite2-0:1.3.14 100% |  11.8 MiB/s | 193.9 KiB |  00m00s
[ 67/196] Installing freetype-0:2.13.3- 100% | 208.0 MiB/s | 851.9 KiB |  00m00s
[ 68/196] Installing harfbuzz-0:11.3.3- 100% | 235.4 MiB/s |   2.8 MiB |  00m00s
[ 69/196] Installing libraqm-0:0.10.1-3 100% |  29.0 MiB/s |  29.7 KiB |  00m00s
[ 70/196] Installing libvmaf-0:3.0.0-4. 100% | 269.7 MiB/s | 828.4 KiB |  00m00s
[ 71/196] Installing libaom-0:3.12.0-3. 100% | 277.4 MiB/s |   5.0 MiB |  00m00s
[ 72/196] Installing rav1e-libs-0:0.7.1 100% | 272.0 MiB/s |   3.0 MiB |  00m00s
[ 73/196] Installing libavif-0:1.1.1-4. 100% | 105.1 MiB/s | 215.2 KiB |  00m00s
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Warning: skipped OpenPGP checks for 194 packages from repositories: copr_base, http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch
Complete!
Finish: build setup for python-dask-2025.4.1-3.fc43~bootstrap.src.rpm
Start: rpmbuild python-dask-2025.4.1-3.fc43~bootstrap.src.rpm
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1753833600
Executing(%mkbuilddir): /bin/sh -e /var/tmp/rpm-tmp.fB4aSi
Executing(%prep): /bin/sh -e /var/tmp/rpm-tmp.N4YBq5
+ umask 022
+ cd /builddir/build/BUILD/python-dask-2025.4.1-build
+ cd /builddir/build/BUILD/python-dask-2025.4.1-build
+ rm -rf dask-2025.4.1
+ /usr/lib/rpm/rpmuncompress -x /builddir/build/SOURCES/dask-2025.4.1.tar.gz
+ STATUS=0
+ '[' 0 -ne 0 ']'
+ cd dask-2025.4.1
+ /usr/bin/chmod -Rf a+rX,u+w,g-w,o-w .
+ /usr/lib/rpm/rpmuncompress /builddir/build/SOURCES/0001-Remove-extra-test-dependencies.patch
+ /usr/bin/patch -p1 -s --fuzz=0 --no-backup-if-mismatch -f
+ /usr/lib/rpm/rpmuncompress /builddir/build/SOURCES/0002-XFAIL-test-if-NotImplementedError-is-raised.patch
+ /usr/bin/patch -p1 -s --fuzz=0 --no-backup-if-mismatch -f
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.Hmo8z7
+ umask 022
+ cd /builddir/build/BUILD/python-dask-2025.4.1-build
+ cd dask-2025.4.1
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(tomli) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir
+ RPM_TOXENV=py314
+ FEDORA=43
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/pyproject-wheeldir --output /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-buildrequires -x test,array,bag,dataframe,delayed
Handling setuptools>=62.6 from build-system.requires
Requirement not satisfied: setuptools>=62.6
Handling versioneer[toml]==0.29 from build-system.requires
Requirement not satisfied: versioneer[toml]==0.29
Exiting dependency generation pass: build backend
+ cat /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-buildrequires
+ rm -rfv '*.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-dask-2025.4.1-3.fc43~bootstrap.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 Additional repo http_kojipkgs_fedorapr 100% |  39.1 KiB/s |   3.8 KiB |  00m00s
 Copr repository                        100% |  15.4 KiB/s |   1.5 KiB |  00m00s
 fedora                                 100% | 204.0 KiB/s |  25.9 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.18.3-2.fc43.noarch" is already installed.
Package "python3-devel-3.14.0~rc1-2.fc43.x86_64" is already installed.
Package "python3-aiohttp-3.11.18-3.fc43.x86_64" is already installed.
Package "python3-Bottleneck-1.4.2-4.fc43.x86_64" is already installed.
Package "python3-crick-0.0.8-6.fc43.x86_64" is already installed.
Package "python3-fastavro-1.11.1-4.fc43.x86_64" is already installed.
Package "python3-graphviz-1:0.20.1-13.fc43.noarch" is already installed.
Package "python3-h5py-3.13.0-3.fc43.x86_64" is already installed.
Package "python3-ipython-8.37.0-4.fc43.noarch" is already installed.
Package "python3-jsonschema-4.23.0-4.fc43.noarch" is already installed.
Package "python3-matplotlib-3.10.3-7.fc43.x86_64" is already installed.
Package "python3-packaging-25.0-4.fc43.noarch" is already installed.
Package "python3-psutil-7.0.0-3.fc43.x86_64" is already installed.
Package "python3-snappy-0.7.3-2.fc43.noarch" is already installed.
Package "python3-requests-2.32.4-3.fc43.noarch" is already installed.
Package "python3-sqlalchemy-2.0.42-1.fc43.x86_64" is already installed.
Package "python3-tables-3.10.1-7.fc43.x86_64" is already installed.
Package "python3-zarr-2.18.7-4.fc43.noarch" is already installed.

Package                  Arch   Version        Repository                                                         Size
Installing:
 python3-pip             noarch 25.1.1-14.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  12.5 MiB
 python3-setuptools      noarch 78.1.1-10.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   9.0 MiB
 python3-versioneer      noarch 0.29-5.fc43    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 277.8 KiB
 python3-versioneer+toml noarch 0.29-5.fc43    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  17.1 KiB

Transaction Summary:
 Installing:         4 packages

Total size of inbound packages is 5 MiB. Need to download 5 MiB.
After this operation, 22 MiB extra will be used (install 22 MiB, remove 0 B).
[1/4] python3-versioneer-0:0.29-5.fc43. 100% | 418.3 KiB/s |  56.1 KiB |  00m00s
[2/4] python3-versioneer+toml-0:0.29-5. 100% | 225.4 KiB/s |   8.8 KiB |  00m00s
[3/4] python3-pip-0:25.1.1-14.fc43.noar 100% |  11.1 MiB/s |   2.6 MiB |  00m00s
[4/4] python3-setuptools-0:78.1.1-10.fc 100% |   7.8 MiB/s |   1.9 MiB |  00m00s
--------------------------------------------------------------------------------
[4/4] Total                             100% |  18.9 MiB/s |   4.6 MiB |  00m00s
Running transaction
[1/6] Verify package files              100% | 235.0   B/s |   4.0   B |  00m00s
[2/6] Prepare transaction               100% |  76.0   B/s |   4.0   B |  00m00s
[3/6] Installing python3-versioneer-0:0 100% |  17.1 MiB/s | 280.5 KiB |  00m00s
[4/6] Installing python3-versioneer+tom 100% |  60.5 KiB/s | 124.0   B |  00m00s
[5/6] Installing python3-setuptools-0:7 100% | 119.8 MiB/s |   9.2 MiB |  00m00s
[6/6] Installing python3-pip-0:25.1.1-1 100% |  86.4 MiB/s |  12.8 MiB |  00m00s
Warning: skipped OpenPGP checks for 4 packages from repository: http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch
Complete!
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1753833600
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.hfMbGe
+ umask 022
+ cd /builddir/build/BUILD/python-dask-2025.4.1-build
+ cd dask-2025.4.1
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(tomli) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir
+ RPM_TOXENV=py314
+ FEDORA=43
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/pyproject-wheeldir --output /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-buildrequires -x test,array,bag,dataframe,delayed
Handling setuptools>=62.6 from build-system.requires
Requirement satisfied: setuptools>=62.6
   (installed: setuptools 78.1.1)
Handling versioneer[toml]==0.29 from build-system.requires
Requirement satisfied: versioneer[toml]==0.29
   (installed: versioneer 0.29)
   (extras are currently not checked)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated
!!

        ********************************************************************************
        Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0).

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  corresp(dist, value, root_dir)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:55: SetuptoolsDeprecationWarning: 'tool.setuptools.license-files' is deprecated in favor of 'project.license-files' (available on setuptools>=77.0.0).
!!

        ********************************************************************************

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license-files for details.
        ********************************************************************************

!!
  _apply_tool_table(dist, config, filename)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  dist._finalize_license_expression()
/usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  self._finalize_license_expression()
running egg_info
writing dask.egg-info/PKG-INFO
writing dependency_links to dask.egg-info/dependency_links.txt
writing entry points to dask.egg-info/entry_points.txt
writing requirements to dask.egg-info/requires.txt
writing top-level names to dask.egg-info/top_level.txt
reading manifest file 'dask.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'versioneer.py'
adding license file 'LICENSE.txt'
adding license file 'dask/array/NUMPY_LICENSE.txt'
writing manifest file 'dask.egg-info/SOURCES.txt'
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated
!!

        ********************************************************************************
        Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0).

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  corresp(dist, value, root_dir)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:55: SetuptoolsDeprecationWarning: 'tool.setuptools.license-files' is deprecated in favor of 'project.license-files' (available on setuptools>=77.0.0).
!!

        ********************************************************************************

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license-files for details.
        ********************************************************************************

!!
  _apply_tool_table(dist, config, filename)
running dist_info
writing dask.egg-info/PKG-INFO
writing dependency_links to dask.egg-info/dependency_links.txt
writing entry points to dask.egg-info/entry_points.txt
writing requirements to dask.egg-info/requires.txt
writing top-level names to dask.egg-info/top_level.txt
reading manifest file 'dask.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'versioneer.py'
adding license file 'LICENSE.txt'
adding license file 'dask/array/NUMPY_LICENSE.txt'
writing manifest file 'dask.egg-info/SOURCES.txt'
creating '/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/dask-2025.4.1.dist-info'
Handling click>=8.1 from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: click>=8.1
Handling cloudpickle>=3.0.0 from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: cloudpickle>=3.0.0
Handling fsspec>=2021.09.0 from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: fsspec>=2021.09.0
Handling packaging>=20.0 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: packaging>=20.0
   (installed: packaging 25.0)
Handling partd>=1.4.0 from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: partd>=1.4.0
Handling pyyaml>=5.3.1 from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: pyyaml>=5.3.1
Handling toolz>=0.10.0 from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: toolz>=0.10.0
Handling importlib_metadata>=4.13.0; python_version < "3.12" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: importlib_metadata>=4.13.0; python_version < "3.12"
Handling numpy>=1.24; extra == "array" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: numpy>=1.24; extra == "array"
   (installed: numpy 2.3.2)
Handling dask[array]; extra == "dataframe" from hook generated metadata: Requires-Dist (dask)
Handling pandas>=2.0; extra == "dataframe" from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: pandas>=2.0; extra == "dataframe"
Handling pyarrow>=14.0.1; extra == "dataframe" from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: pyarrow>=14.0.1; extra == "dataframe"
Handling distributed==2025.4.1; extra == "distributed" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: distributed==2025.4.1; extra == "distributed"
Handling bokeh>=3.1.0; extra == "diagnostics" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: bokeh>=3.1.0; extra == "diagnostics"
Handling jinja2>=2.10.3; extra == "diagnostics" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: jinja2>=2.10.3; extra == "diagnostics"
Handling dask[array,dataframe,diagnostics,distributed]; extra == "complete" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: dask[array,dataframe,diagnostics,distributed]; extra == "complete"
Handling pyarrow>=14.0.1; extra == "complete" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: pyarrow>=14.0.1; extra == "complete"
Handling lz4>=4.3.2; extra == "complete" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: lz4>=4.3.2; extra == "complete"
Handling pandas[test]; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: pandas[test]; extra == "test"
Handling pytest; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: pytest; extra == "test"
Handling pytest-mock; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: pytest-mock; extra == "test"
Handling pytest-rerunfailures; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: pytest-rerunfailures; extra == "test"
Handling pytest-timeout; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: pytest-timeout; extra == "test"
Handling pytest-xdist; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement not satisfied: pytest-xdist; extra == "test"
+ cat /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-buildrequires
+ rm -rfv dask-2025.4.1.dist-info/
removed 'dask-2025.4.1.dist-info/licenses/dask/array/NUMPY_LICENSE.txt'
removed directory 'dask-2025.4.1.dist-info/licenses/dask/array'
removed directory 'dask-2025.4.1.dist-info/licenses/dask'
removed 'dask-2025.4.1.dist-info/licenses/LICENSE.txt'
removed directory 'dask-2025.4.1.dist-info/licenses'
removed 'dask-2025.4.1.dist-info/METADATA'
removed 'dask-2025.4.1.dist-info/entry_points.txt'
removed 'dask-2025.4.1.dist-info/top_level.txt'
removed directory 'dask-2025.4.1.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-dask-2025.4.1-3.fc43~bootstrap.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 Additional repo http_kojipkgs_fedorapr 100% |  37.5 KiB/s |   3.8 KiB |  00m00s
 Copr repository                        100% |  15.0 KiB/s |   1.5 KiB |  00m00s
 fedora                                 100% | 199.3 KiB/s |  25.9 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.18.3-2.fc43.noarch" is already installed.
Package "python3-devel-3.14.0~rc1-2.fc43.x86_64" is already installed.
Package "python3-aiohttp-3.11.18-3.fc43.x86_64" is already installed.
Package "python3-Bottleneck-1.4.2-4.fc43.x86_64" is already installed.
Package "python3-crick-0.0.8-6.fc43.x86_64" is already installed.
Package "python3-fastavro-1.11.1-4.fc43.x86_64" is already installed.
Package "python3-graphviz-1:0.20.1-13.fc43.noarch" is already installed.
Package "python3-h5py-3.13.0-3.fc43.x86_64" is already installed.
Package "python3-ipython-8.37.0-4.fc43.noarch" is already installed.
Package "python3-jsonschema-4.23.0-4.fc43.noarch" is already installed.
Package "python3-matplotlib-3.10.3-7.fc43.x86_64" is already installed.
Package "python3-numpy-1:2.3.2-1.fc43.x86_64" is already installed.
Package "python3-packaging-25.0-4.fc43.noarch" is already installed.
Package "python3-packaging-25.0-4.fc43.noarch" is already installed.
Package "python3-pip-25.1.1-14.fc43.noarch" is already installed.
Package "python3-psutil-7.0.0-3.fc43.x86_64" is already installed.
Package "python3-snappy-0.7.3-2.fc43.noarch" is already installed.
Package "python3-requests-2.32.4-3.fc43.noarch" is already installed.
Package "python3-setuptools-78.1.1-10.fc43.noarch" is already installed.
Package "python3-sqlalchemy-2.0.42-1.fc43.x86_64" is already installed.
Package "python3-tables-3.10.1-7.fc43.x86_64" is already installed.
Package "python3-versioneer-0.29-5.fc43.noarch" is already installed.
Package "python3-versioneer+toml-0.29-5.fc43.noarch" is already installed.
Package "python3-zarr-2.18.7-4.fc43.noarch" is already installed.

Package                       Arch   Version                   Repository                                                         Size
Installing:
 python3-click                noarch 8.1.7-9.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.2 MiB
 python3-cloudpickle          noarch 3.1.1-5.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 129.3 KiB
 python3-fsspec               noarch 2025.5.1-5.fc43~bootstrap http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.9 MiB
 python3-pandas               x86_64 2.2.3-5.fc43~bootstrap    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  43.5 MiB
 python3-pandas+test          x86_64 2.2.3-5.fc43~bootstrap    http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  47.1 MiB
 python3-partd                noarch 1.4.2-6.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 166.8 KiB
 python3-pyarrow              x86_64 20.0.0-4.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  26.7 MiB
 python3-pytest               noarch 8.3.5-4.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   4.1 MiB
 python3-pytest-mock          noarch 3.14.1-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 128.8 KiB
 python3-pytest-rerunfailures noarch 15.0-4.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  84.9 KiB
 python3-pytest-timeout       noarch 2.4.0-3.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  82.1 KiB
 python3-pytest-xdist         noarch 3.7.0-3.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 468.4 KiB
 python3-pyyaml               x86_64 6.0.2-4.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 793.4 KiB
 python3-toolz                noarch 1.0.0-4.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 618.0 KiB
Installing dependencies:
 abseil-cpp                   x86_64 20250512.0-2.fc43         http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.3 MiB
 boost-program-options        x86_64 1.83.0-14.fc43            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 280.8 KiB
 c-ares                       x86_64 1.34.5-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 269.1 KiB
 grpc                         x86_64 1.48.4-50.fc43            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  10.5 MiB
 grpc-cpp                     x86_64 1.48.4-50.fc43            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.0 MiB
 grpc-data                    noarch 1.48.4-50.fc43            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  29.6 KiB
 libarrow                     x86_64 20.0.0-4.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  26.5 MiB
 libarrow-acero-libs          x86_64 20.0.0-4.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.4 MiB
 libarrow-dataset-libs        x86_64 20.0.0-4.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.5 MiB
 libarrow-doc                 noarch 20.0.0-4.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 117.1 KiB
 libarrow-flight-libs         x86_64 20.0.0-4.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.4 MiB
 libarrow-glib-libs           x86_64 20.0.0-4.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.5 MiB
 libicu                       x86_64 76.1-5.fc43               http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  36.3 MiB
 liborc2                      x86_64 2.1.3-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   1.8 MiB
 libyaml                      x86_64 0.2.5-17.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 134.6 KiB
 parquet-libs                 x86_64 20.0.0-4.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.3 MiB
 protobuf                     x86_64 3.19.6-15.fc43            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   3.3 MiB
 protobuf-compiler            x86_64 3.19.6-15.fc43            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   2.5 MiB
 python3-execnet              noarch 2.1.1-8.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 970.2 KiB
 python3-hypothesis           noarch 6.123.0-5.fc43            http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   5.0 MiB
 python3-iniconfig            noarch 1.1.1-28.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  21.0 KiB
 python3-locket               noarch 1.0.0-16.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch  20.9 KiB
 python3-pluggy               noarch 1.6.0-2.fc43              http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 211.0 KiB
 python3-pytz                 noarch 2025.2-3.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 224.0 KiB
 python3-sortedcontainers     noarch 2.4.0-22.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 393.4 KiB
 re2                          x86_64 1:20240702-35.fc43        http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 565.8 KiB
 thrift                       x86_64 0.20.0-6.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch   5.5 MiB
 utf8proc                     x86_64 2.10.0-2.fc43             http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 372.0 KiB

Transaction Summary:
 Installing:        42 packages

Total size of inbound packages is 52 MiB. Need to download 52 MiB.
After this operation, 236 MiB extra will be used (install 236 MiB, remove 0 B).
[ 1/42] python3-cloudpickle-0:3.1.1-5.f 100% | 345.7 KiB/s |  48.1 KiB |  00m00s
[ 2/42] python3-click-0:8.1.7-9.fc43.no 100% |   1.3 MiB/s | 244.0 KiB |  00m00s
[ 3/42] python3-fsspec-0:2025.5.1-5.fc4 100% |   2.3 MiB/s | 435.5 KiB |  00m00s
[ 4/42] python3-partd-0:1.4.2-6.fc43.no 100% |   1.2 MiB/s |  54.9 KiB |  00m00s
[ 5/42] python3-pandas+test-0:2.2.3-5.f 100% |  40.4 MiB/s |   7.1 MiB |  00m00s
[ 6/42] python3-pandas-0:2.2.3-5.fc43~b 100% |  30.5 MiB/s |   8.1 MiB |  00m00s
[ 7/42] python3-pytest-0:8.3.5-4.fc43.n 100% |  13.5 MiB/s | 773.9 KiB |  00m00s
[ 8/42] python3-pytest-mock-0:3.14.1-2. 100% | 973.6 KiB/s |  38.9 KiB |  00m00s
[ 9/42] python3-pytest-rerunfailures-0: 100% | 763.0 KiB/s |  32.0 KiB |  00m00s
[10/42] python3-pytest-timeout-0:2.4.0- 100% | 761.8 KiB/s |  30.5 KiB |  00m00s
[11/42] python3-pytest-xdist-0:3.7.0-3. 100% |   2.6 MiB/s | 107.2 KiB |  00m00s
[12/42] python3-pyyaml-0:6.0.2-4.fc43.x 100% |   5.7 MiB/s | 227.0 KiB |  00m00s
[13/42] python3-toolz-0:1.0.0-4.fc43.no 100% |   3.6 MiB/s | 158.6 KiB |  00m00s
[14/42] libyaml-0:0.2.5-17.fc43.x86_64  100% |   1.5 MiB/s |  60.1 KiB |  00m00s
[15/42] python3-execnet-0:2.1.1-8.fc43. 100% |   5.9 MiB/s | 249.8 KiB |  00m00s
[16/42] python3-iniconfig-0:1.1.1-28.fc 100% | 482.1 KiB/s |  18.3 KiB |  00m00s
[17/42] python3-pluggy-0:1.6.0-2.fc43.n 100% |   1.5 MiB/s |  55.9 KiB |  00m00s
[18/42] libarrow-acero-libs-0:20.0.0-4. 100% |  10.7 MiB/s | 516.0 KiB |  00m00s
[19/42] libarrow-dataset-libs-0:20.0.0- 100% |  10.9 MiB/s | 511.1 KiB |  00m00s
[20/42] libarrow-0:20.0.0-4.fc43.x86_64 100% |  49.9 MiB/s |   6.2 MiB |  00m00s
[21/42] libarrow-flight-libs-0:20.0.0-4 100% |   8.9 MiB/s | 421.3 KiB |  00m00s
[22/42] parquet-libs-0:20.0.0-4.fc43.x8 100% |  20.4 MiB/s |   1.0 MiB |  00m00s
[23/42] python3-locket-0:1.0.0-16.fc43. 100% | 450.9 KiB/s |  18.0 KiB |  00m00s
[24/42] python3-pytz-0:2025.2-3.fc43.no 100% |   1.4 MiB/s |  61.5 KiB |  00m00s
[25/42] python3-hypothesis-0:6.123.0-5. 100% |  23.0 MiB/s |   1.2 MiB |  00m00s
[26/42] python3-sortedcontainers-0:2.4. 100% |   1.7 MiB/s |  63.6 KiB |  00m00s
[27/42] boost-program-options-0:1.83.0- 100% |   2.7 MiB/s | 110.6 KiB |  00m00s
[28/42] libarrow-doc-0:20.0.0-4.fc43.no 100% | 764.0 KiB/s |  29.0 KiB |  00m00s
[29/42] thrift-0:0.20.0-6.fc43.x86_64   100% |  27.7 MiB/s |   1.8 MiB |  00m00s
[30/42] abseil-cpp-0:20250512.0-2.fc43. 100% |   5.5 MiB/s | 711.2 KiB |  00m00s
[31/42] grpc-0:1.48.4-50.fc43.x86_64    100% |  34.1 MiB/s |   2.6 MiB |  00m00s
[32/42] grpc-cpp-0:1.48.4-50.fc43.x86_6 100% |  15.6 MiB/s | 718.5 KiB |  00m00s
[33/42] libarrow-glib-libs-0:20.0.0-4.f 100% |   9.2 MiB/s | 422.5 KiB |  00m00s
[34/42] protobuf-0:3.19.6-15.fc43.x86_6 100% |  20.1 MiB/s |   1.0 MiB |  00m00s
[35/42] liborc2-0:2.1.3-2.fc43.x86_64   100% |  12.0 MiB/s | 551.3 KiB |  00m00s
[36/42] re2-1:20240702-35.fc43.x86_64   100% |   5.4 MiB/s | 225.0 KiB |  00m00s
[37/42] utf8proc-0:2.10.0-2.fc43.x86_64 100% |   2.1 MiB/s |  81.8 KiB |  00m00s
[38/42] protobuf-compiler-0:3.19.6-15.f 100% |  16.6 MiB/s | 783.0 KiB |  00m00s
[39/42] c-ares-0:1.34.5-2.fc43.x86_64   100% |   2.5 MiB/s | 117.0 KiB |  00m00s
[40/42] grpc-data-0:1.48.4-50.fc43.noar 100% | 430.6 KiB/s |  19.4 KiB |  00m00s
[41/42] python3-pyarrow-0:20.0.0-4.fc43 100% |   4.2 MiB/s |   4.8 MiB |  00m01s
[42/42] libicu-0:76.1-5.fc43.x86_64     100% |  44.0 MiB/s |  10.7 MiB |  00m00s
--------------------------------------------------------------------------------
[42/42] Total                           100% |  35.7 MiB/s |  52.2 MiB |  00m01s
Running transaction
[ 1/44] Verify package files            100% | 214.0   B/s |  42.0   B |  00m00s
[ 2/44] Prepare transaction             100% | 411.0   B/s |  42.0   B |  00m00s
[ 3/44] Installing libarrow-doc-0:20.0. 100% |  57.6 MiB/s | 117.9 KiB |  00m00s
[ 4/44] Installing protobuf-0:3.19.6-15 100% | 272.5 MiB/s |   3.3 MiB |  00m00s
[ 5/44] Installing abseil-cpp-0:2025051 100% | 111.6 MiB/s |   2.3 MiB |  00m00s
[ 6/44] Installing liborc2-0:2.1.3-2.fc 100% | 257.0 MiB/s |   1.8 MiB |  00m00s
[ 7/44] Installing protobuf-compiler-0: 100% | 108.3 MiB/s |   2.5 MiB |  00m00s
[ 8/44] Installing libicu-0:76.1-5.fc43 100% | 302.6 MiB/s |  36.3 MiB |  00m00s
[ 9/44] Installing re2-1:20240702-35.fc 100% | 184.6 MiB/s | 567.2 KiB |  00m00s
[10/44] Installing grpc-data-0:1.48.4-5 100% |  29.7 MiB/s |  30.4 KiB |  00m00s
[11/44] Installing c-ares-0:1.34.5-2.fc 100% | 132.1 MiB/s | 270.6 KiB |  00m00s
[12/44] Installing grpc-0:1.48.4-50.fc4 100% | 276.9 MiB/s |  10.5 MiB |  00m00s
[13/44] Installing grpc-cpp-0:1.48.4-50 100% | 199.6 MiB/s |   3.0 MiB |  00m00s
[14/44] Installing utf8proc-0:2.10.0-2. 100% | 121.6 MiB/s | 373.4 KiB |  00m00s
[15/44] Installing libarrow-0:20.0.0-4. 100% | 273.6 MiB/s |  26.5 MiB |  00m00s
[16/44] Installing libarrow-acero-libs- 100% | 279.6 MiB/s |   1.4 MiB |  00m00s
[17/44] Installing libarrow-glib-libs-0 100% | 301.9 MiB/s |   1.5 MiB |  00m00s
[18/44] Installing libarrow-flight-libs 100% | 281.1 MiB/s |   1.4 MiB |  00m00s
[19/44] Installing thrift-0:0.20.0-6.fc 100% | 165.5 MiB/s |   5.5 MiB |  00m00s
[20/44] Installing boost-program-option 100% | 137.6 MiB/s | 281.8 KiB |  00m00s
[21/44] Installing parquet-libs-0:20.0. 100% | 329.4 MiB/s |   3.3 MiB |  00m00s
[22/44] Installing libarrow-dataset-lib 100% | 294.1 MiB/s |   1.5 MiB |  00m00s
[23/44] Installing python3-sortedcontai 100% |  97.1 MiB/s | 397.8 KiB |  00m00s
[24/44] Installing python3-hypothesis-0 100% | 117.8 MiB/s |   5.1 MiB |  00m00s
[25/44] Installing python3-pytz-0:2025. 100% |  44.8 MiB/s | 229.4 KiB |  00m00s
[26/44] Installing python3-pandas-0:2.2 100% | 240.5 MiB/s |  43.8 MiB |  00m00s
[27/44] Installing python3-locket-0:1.0 100% |  11.4 MiB/s |  23.4 KiB |  00m00s
[28/44] Installing python3-pluggy-0:1.6 100% |  70.8 MiB/s | 217.5 KiB |  00m00s
[29/44] Installing python3-iniconfig-0: 100% |  11.8 MiB/s |  24.2 KiB |  00m00s
[30/44] Installing python3-pytest-0:8.3 100% | 111.8 MiB/s |   4.1 MiB |  00m00s
[31/44] Installing python3-execnet-0:2. 100% | 107.6 MiB/s | 991.8 KiB |  00m00s
[32/44] Installing python3-pytest-xdist 100% |  93.9 MiB/s | 480.8 KiB |  00m00s
[33/44] Installing libyaml-0:0.2.5-17.f 100% | 132.8 MiB/s | 136.0 KiB |  00m00s
[34/44] Installing python3-toolz-0:1.0. 100% |  77.8 MiB/s | 637.7 KiB |  00m00s
[35/44] Installing python3-partd-0:1.4. 100% |  57.3 MiB/s | 176.2 KiB |  00m00s
[36/44] Installing python3-pyyaml-0:6.0 100% |  65.7 MiB/s | 807.3 KiB |  00m00s
[37/44] Installing python3-pandas+test- 100% | 164.1 MiB/s |  47.8 MiB |  00m00s
[38/44] Installing python3-pytest-mock- 100% |  43.6 MiB/s | 133.9 KiB |  00m00s
[39/44] Installing python3-pytest-rerun 100% |  85.5 MiB/s |  87.6 KiB |  00m00s
[40/44] Installing python3-pytest-timeo 100% |  11.8 MiB/s |  84.6 KiB |  00m00s
[41/44] Installing python3-pyarrow-0:20 100% | 273.9 MiB/s |  26.8 MiB |  00m00s
[42/44] Installing python3-fsspec-0:202 100% | 137.5 MiB/s |   1.9 MiB |  00m00s
[43/44] Installing python3-cloudpickle- 100% |  43.3 MiB/s | 133.2 KiB |  00m00s
[44/44] Installing python3-click-0:8.1. 100% |  19.8 MiB/s |   1.2 MiB |  00m00s
Warning: skipped OpenPGP checks for 42 packages from repository: http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch
Complete!
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1753833600
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.Pjljhn
+ umask 022
+ cd /builddir/build/BUILD/python-dask-2025.4.1-build
+ cd dask-2025.4.1
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(tomli) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir
+ RPM_TOXENV=py314
+ FEDORA=43
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/pyproject-wheeldir --output /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-buildrequires -x test,array,bag,dataframe,delayed
Handling setuptools>=62.6 from build-system.requires
Requirement satisfied: setuptools>=62.6
   (installed: setuptools 78.1.1)
Handling versioneer[toml]==0.29 from build-system.requires
Requirement satisfied: versioneer[toml]==0.29
   (installed: versioneer 0.29)
   (extras are currently not checked)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated
!!

        ********************************************************************************
        Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0).

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  corresp(dist, value, root_dir)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:55: SetuptoolsDeprecationWarning: 'tool.setuptools.license-files' is deprecated in favor of 'project.license-files' (available on setuptools>=77.0.0).
!!

        ********************************************************************************

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license-files for details.
        ********************************************************************************

!!
  _apply_tool_table(dist, config, filename)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  dist._finalize_license_expression()
/usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  self._finalize_license_expression()
running egg_info
writing dask.egg-info/PKG-INFO
writing dependency_links to dask.egg-info/dependency_links.txt
writing entry points to dask.egg-info/entry_points.txt
writing requirements to dask.egg-info/requires.txt
writing top-level names to dask.egg-info/top_level.txt
reading manifest file 'dask.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'versioneer.py'
adding license file 'LICENSE.txt'
adding license file 'dask/array/NUMPY_LICENSE.txt'
writing manifest file 'dask.egg-info/SOURCES.txt'
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated
!!

        ********************************************************************************
        Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0).

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  corresp(dist, value, root_dir)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:55: SetuptoolsDeprecationWarning: 'tool.setuptools.license-files' is deprecated in favor of 'project.license-files' (available on setuptools>=77.0.0).
!!

        ********************************************************************************

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license-files for details.
        ********************************************************************************

!!
  _apply_tool_table(dist, config, filename)
running dist_info
writing dask.egg-info/PKG-INFO
writing dependency_links to dask.egg-info/dependency_links.txt
writing entry points to dask.egg-info/entry_points.txt
writing requirements to dask.egg-info/requires.txt
writing top-level names to dask.egg-info/top_level.txt
reading manifest file 'dask.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'versioneer.py'
adding license file 'LICENSE.txt'
adding license file 'dask/array/NUMPY_LICENSE.txt'
writing manifest file 'dask.egg-info/SOURCES.txt'
creating '/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/dask-2025.4.1.dist-info'
Handling click>=8.1 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: click>=8.1
   (installed: click 8.1.7)
Handling cloudpickle>=3.0.0 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: cloudpickle>=3.0.0
   (installed: cloudpickle 3.1.1)
Handling fsspec>=2021.09.0 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: fsspec>=2021.09.0
   (installed: fsspec 2025.5.1)
Handling packaging>=20.0 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: packaging>=20.0
   (installed: packaging 25.0)
Handling partd>=1.4.0 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: partd>=1.4.0
   (installed: partd 1.4.2)
Handling pyyaml>=5.3.1 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pyyaml>=5.3.1
   (installed: pyyaml 6.0.2)
Handling toolz>=0.10.0 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: toolz>=0.10.0
   (installed: toolz 1.0.0)
Handling importlib_metadata>=4.13.0; python_version < "3.12" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: importlib_metadata>=4.13.0; python_version < "3.12"
Handling numpy>=1.24; extra == "array" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: numpy>=1.24; extra == "array"
   (installed: numpy 2.3.2)
Handling dask[array]; extra == "dataframe" from hook generated metadata: Requires-Dist (dask)
Handling pandas>=2.0; extra == "dataframe" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pandas>=2.0; extra == "dataframe"
   (installed: pandas 2.2.3)
Handling pyarrow>=14.0.1; extra == "dataframe" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pyarrow>=14.0.1; extra == "dataframe"
   (installed: pyarrow 20.0.0)
Handling distributed==2025.4.1; extra == "distributed" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: distributed==2025.4.1; extra == "distributed"
Handling bokeh>=3.1.0; extra == "diagnostics" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: bokeh>=3.1.0; extra == "diagnostics"
Handling jinja2>=2.10.3; extra == "diagnostics" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: jinja2>=2.10.3; extra == "diagnostics"
Handling dask[array,dataframe,diagnostics,distributed]; extra == "complete" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: dask[array,dataframe,diagnostics,distributed]; extra == "complete"
Handling pyarrow>=14.0.1; extra == "complete" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: pyarrow>=14.0.1; extra == "complete"
Handling lz4>=4.3.2; extra == "complete" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: lz4>=4.3.2; extra == "complete"
Handling pandas[test]; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pandas[test]; extra == "test"
   (installed: pandas 2.2.3)
   (extras are currently not checked)
Handling pytest; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pytest; extra == "test"
   (installed: pytest 8.3.5)
Handling pytest-mock; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pytest-mock; extra == "test"
   (installed: pytest-mock 3.14.1)
Handling pytest-rerunfailures; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pytest-rerunfailures; extra == "test"
   (installed: pytest-rerunfailures 15.0)
Handling pytest-timeout; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pytest-timeout; extra == "test"
   (installed: pytest-timeout 2.4.0)
Handling pytest-xdist; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pytest-xdist; extra == "test"
   (installed: pytest-xdist 3.7.0)
+ cat /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-buildrequires
+ rm -rfv dask-2025.4.1.dist-info/
removed 'dask-2025.4.1.dist-info/licenses/dask/array/NUMPY_LICENSE.txt'
removed directory 'dask-2025.4.1.dist-info/licenses/dask/array'
removed directory 'dask-2025.4.1.dist-info/licenses/dask'
removed 'dask-2025.4.1.dist-info/licenses/LICENSE.txt'
removed directory 'dask-2025.4.1.dist-info/licenses'
removed 'dask-2025.4.1.dist-info/METADATA'
removed 'dask-2025.4.1.dist-info/top_level.txt'
removed 'dask-2025.4.1.dist-info/entry_points.txt'
removed directory 'dask-2025.4.1.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-dask-2025.4.1-3.fc43~bootstrap.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 Additional repo http_kojipkgs_fedorapr 100% |  36.1 KiB/s |   3.8 KiB |  00m00s
 Copr repository                        100% |  14.1 KiB/s |   1.5 KiB |  00m00s
 fedora                                 100% | 189.1 KiB/s |  25.9 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.18.3-2.fc43.noarch" is already installed.
Nothing to do.
Package "python3-devel-3.14.0~rc1-2.fc43.x86_64" is already installed.
Package "python3-aiohttp-3.11.18-3.fc43.x86_64" is already installed.
Package "python3-Bottleneck-1.4.2-4.fc43.x86_64" is already installed.
Package "python3-click-8.1.7-9.fc43.noarch" is already installed.
Package "python3-cloudpickle-3.1.1-5.fc43.noarch" is already installed.
Package "python3-crick-0.0.8-6.fc43.x86_64" is already installed.
Package "python3-fastavro-1.11.1-4.fc43.x86_64" is already installed.
Package "python3-fsspec-2025.5.1-5.fc43~bootstrap.noarch" is already installed.
Package "python3-graphviz-1:0.20.1-13.fc43.noarch" is already installed.
Package "python3-h5py-3.13.0-3.fc43.x86_64" is already installed.
Package "python3-ipython-8.37.0-4.fc43.noarch" is already installed.
Package "python3-jsonschema-4.23.0-4.fc43.noarch" is already installed.
Package "python3-matplotlib-3.10.3-7.fc43.x86_64" is already installed.
Package "python3-numpy-1:2.3.2-1.fc43.x86_64" is already installed.
Package "python3-packaging-25.0-4.fc43.noarch" is already installed.
Package "python3-packaging-25.0-4.fc43.noarch" is already installed.
Package "python3-pandas-2.2.3-5.fc43~bootstrap.x86_64" is already installed.
Package "python3-pandas-2.2.3-5.fc43~bootstrap.x86_64" is already installed.
Package "python3-pandas+test-2.2.3-5.fc43~bootstrap.x86_64" is already installed.
Package "python3-partd-1.4.2-6.fc43.noarch" is already installed.
Package "python3-pip-25.1.1-14.fc43.noarch" is already installed.
Package "python3-psutil-7.0.0-3.fc43.x86_64" is already installed.
Package "python3-pyarrow-20.0.0-4.fc43.x86_64" is already installed.
Package "python3-pytest-8.3.5-4.fc43.noarch" is already installed.
Package "python3-pytest-mock-3.14.1-2.fc43.noarch" is already installed.
Package "python3-pytest-rerunfailures-15.0-4.fc43.noarch" is already installed.
Package "python3-pytest-timeout-2.4.0-3.fc43.noarch" is already installed.
Package "python3-pytest-xdist-3.7.0-3.fc43.noarch" is already installed.
Package "python3-snappy-0.7.3-2.fc43.noarch" is already installed.
Package "python3-pyyaml-6.0.2-4.fc43.x86_64" is already installed.
Package "python3-requests-2.32.4-3.fc43.noarch" is already installed.
Package "python3-setuptools-78.1.1-10.fc43.noarch" is already installed.
Package "python3-sqlalchemy-2.0.42-1.fc43.x86_64" is already installed.
Package "python3-tables-3.10.1-7.fc43.x86_64" is already installed.
Package "python3-toolz-1.0.0-4.fc43.noarch" is already installed.
Package "python3-versioneer-0.29-5.fc43.noarch" is already installed.
Package "python3-versioneer+toml-0.29-5.fc43.noarch" is already installed.
Package "python3-zarr-2.18.7-4.fc43.noarch" is already installed.

Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1753833600
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.Xsml8x
+ umask 022
+ cd /builddir/build/BUILD/python-dask-2025.4.1-build
+ cd dask-2025.4.1
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ echo '(python3dist(tomli) if python3-devel < 3.11)'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir
+ RPM_TOXENV=py314
+ FEDORA=43
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/pyproject-wheeldir --output /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-buildrequires -x test,array,bag,dataframe,delayed
Handling setuptools>=62.6 from build-system.requires
Requirement satisfied: setuptools>=62.6
   (installed: setuptools 78.1.1)
Handling versioneer[toml]==0.29 from build-system.requires
Requirement satisfied: versioneer[toml]==0.29
   (installed: versioneer 0.29)
   (extras are currently not checked)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated
!!

        ********************************************************************************
        Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0).

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  corresp(dist, value, root_dir)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:55: SetuptoolsDeprecationWarning: 'tool.setuptools.license-files' is deprecated in favor of 'project.license-files' (available on setuptools>=77.0.0).
!!

        ********************************************************************************

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license-files for details.
        ********************************************************************************

!!
  _apply_tool_table(dist, config, filename)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  dist._finalize_license_expression()
/usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
!!

        ********************************************************************************
        Please consider removing the following classifiers in favor of a SPDX license expression:

        License :: OSI Approved :: BSD License

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  self._finalize_license_expression()
running egg_info
writing dask.egg-info/PKG-INFO
writing dependency_links to dask.egg-info/dependency_links.txt
writing entry points to dask.egg-info/entry_points.txt
writing requirements to dask.egg-info/requires.txt
writing top-level names to dask.egg-info/top_level.txt
reading manifest file 'dask.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'versioneer.py'
adding license file 'LICENSE.txt'
adding license file 'dask/array/NUMPY_LICENSE.txt'
writing manifest file 'dask.egg-info/SOURCES.txt'
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated
!!

        ********************************************************************************
        Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0).

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
        ********************************************************************************

!!
  corresp(dist, value, root_dir)
/usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:55: SetuptoolsDeprecationWarning: 'tool.setuptools.license-files' is deprecated in favor of 'project.license-files' (available on setuptools>=77.0.0).
!!

        ********************************************************************************

        By 2026-Feb-18, you need to update your project and remove deprecated calls
        or your builds will no longer be supported.

        See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license-files for details.
        ********************************************************************************

!!
  _apply_tool_table(dist, config, filename)
running dist_info
writing dask.egg-info/PKG-INFO
writing dependency_links to dask.egg-info/dependency_links.txt
writing entry points to dask.egg-info/entry_points.txt
writing requirements to dask.egg-info/requires.txt
writing top-level names to dask.egg-info/top_level.txt
reading manifest file 'dask.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'versioneer.py'
adding license file 'LICENSE.txt'
adding license file 'dask/array/NUMPY_LICENSE.txt'
writing manifest file 'dask.egg-info/SOURCES.txt'
creating '/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/dask-2025.4.1.dist-info'
Handling click>=8.1 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: click>=8.1
   (installed: click 8.1.7)
Handling cloudpickle>=3.0.0 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: cloudpickle>=3.0.0
   (installed: cloudpickle 3.1.1)
Handling fsspec>=2021.09.0 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: fsspec>=2021.09.0
   (installed: fsspec 2025.5.1)
Handling packaging>=20.0 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: packaging>=20.0
   (installed: packaging 25.0)
Handling partd>=1.4.0 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: partd>=1.4.0
   (installed: partd 1.4.2)
Handling pyyaml>=5.3.1 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pyyaml>=5.3.1
   (installed: pyyaml 6.0.2)
Handling toolz>=0.10.0 from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: toolz>=0.10.0
   (installed: toolz 1.0.0)
Handling importlib_metadata>=4.13.0; python_version < "3.12" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: importlib_metadata>=4.13.0; python_version < "3.12"
Handling numpy>=1.24; extra == "array" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: numpy>=1.24; extra == "array"
   (installed: numpy 2.3.2)
Handling dask[array]; extra == "dataframe" from hook generated metadata: Requires-Dist (dask)
Handling pandas>=2.0; extra == "dataframe" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pandas>=2.0; extra == "dataframe"
   (installed: pandas 2.2.3)
Handling pyarrow>=14.0.1; extra == "dataframe" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pyarrow>=14.0.1; extra == "dataframe"
   (installed: pyarrow 20.0.0)
Handling distributed==2025.4.1; extra == "distributed" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: distributed==2025.4.1; extra == "distributed"
Handling bokeh>=3.1.0; extra == "diagnostics" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: bokeh>=3.1.0; extra == "diagnostics"
Handling jinja2>=2.10.3; extra == "diagnostics" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: jinja2>=2.10.3; extra == "diagnostics"
Handling dask[array,dataframe,diagnostics,distributed]; extra == "complete" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: dask[array,dataframe,diagnostics,distributed]; extra == "complete"
Handling pyarrow>=14.0.1; extra == "complete" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: pyarrow>=14.0.1; extra == "complete"
Handling lz4>=4.3.2; extra == "complete" from hook generated metadata: Requires-Dist (dask)
Ignoring alien requirement: lz4>=4.3.2; extra == "complete"
Handling pandas[test]; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pandas[test]; extra == "test"
   (installed: pandas 2.2.3)
   (extras are currently not checked)
Handling pytest; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pytest; extra == "test"
   (installed: pytest 8.3.5)
Handling pytest-mock; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pytest-mock; extra == "test"
   (installed: pytest-mock 3.14.1)
Handling pytest-rerunfailures; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pytest-rerunfailures; extra == "test"
   (installed: pytest-rerunfailures 15.0)
Handling pytest-timeout; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pytest-timeout; extra == "test"
   (installed: pytest-timeout 2.4.0)
Handling pytest-xdist; extra == "test" from hook generated metadata: Requires-Dist (dask)
Requirement satisfied: pytest-xdist; extra == "test"
   (installed: pytest-xdist 3.7.0)
+ cat /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-buildrequires
+ rm -rfv dask-2025.4.1.dist-info/
removed 'dask-2025.4.1.dist-info/licenses/dask/array/NUMPY_LICENSE.txt'
removed directory 'dask-2025.4.1.dist-info/licenses/dask/array'
removed directory 'dask-2025.4.1.dist-info/licenses/dask'
removed 'dask-2025.4.1.dist-info/licenses/LICENSE.txt'
removed directory 'dask-2025.4.1.dist-info/licenses'
removed 'dask-2025.4.1.dist-info/METADATA'
removed 'dask-2025.4.1.dist-info/entry_points.txt'
removed 'dask-2025.4.1.dist-info/top_level.txt'
removed directory 'dask-2025.4.1.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%build): /bin/sh -e /var/tmp/rpm-tmp.uGmsm8
+ umask 022
+ cd /builddir/build/BUILD/python-dask-2025.4.1-build
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ export RUSTFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd dask-2025.4.1
+ mkdir -p /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_wheel.py /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/pyproject-wheeldir
Processing /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1
  Preparing metadata (pyproject.toml): started
  Running command Preparing metadata (pyproject.toml)
  /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated
  !!

          ********************************************************************************
          Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0).

          By 2026-Feb-18, you need to update your project and remove deprecated calls
          or your builds will no longer be supported.

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
          ********************************************************************************

  !!
    corresp(dist, value, root_dir)
  /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:55: SetuptoolsDeprecationWarning: 'tool.setuptools.license-files' is deprecated in favor of 'project.license-files' (available on setuptools>=77.0.0).
  !!

          ********************************************************************************

          By 2026-Feb-18, you need to update your project and remove deprecated calls
          or your builds will no longer be supported.

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license-files for details.
          ********************************************************************************

  !!
    _apply_tool_table(dist, config, filename)
  /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
  !!

          ********************************************************************************
          Please consider removing the following classifiers in favor of a SPDX license expression:

          License :: OSI Approved :: BSD License

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
          ********************************************************************************

  !!
    dist._finalize_license_expression()
  /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
  !!

          ********************************************************************************
          Please consider removing the following classifiers in favor of a SPDX license expression:

          License :: OSI Approved :: BSD License

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
          ********************************************************************************

  !!
    self._finalize_license_expression()
  running dist_info
  creating /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir/pip-modern-metadata-sra6apgh/dask.egg-info
  writing /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir/pip-modern-metadata-sra6apgh/dask.egg-info/PKG-INFO
  writing dependency_links to /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir/pip-modern-metadata-sra6apgh/dask.egg-info/dependency_links.txt
  writing entry points to /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir/pip-modern-metadata-sra6apgh/dask.egg-info/entry_points.txt
  writing requirements to /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir/pip-modern-metadata-sra6apgh/dask.egg-info/requires.txt
  writing top-level names to /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir/pip-modern-metadata-sra6apgh/dask.egg-info/top_level.txt
  writing manifest file '/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir/pip-modern-metadata-sra6apgh/dask.egg-info/SOURCES.txt'
  reading manifest file '/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir/pip-modern-metadata-sra6apgh/dask.egg-info/SOURCES.txt'
  reading manifest template 'MANIFEST.in'
  warning: no files found matching 'versioneer.py'
  adding license file 'LICENSE.txt'
  adding license file 'dask/array/NUMPY_LICENSE.txt'
  writing manifest file '/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir/pip-modern-metadata-sra6apgh/dask.egg-info/SOURCES.txt'
  creating '/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir/pip-modern-metadata-sra6apgh/dask-2025.4.1.dist-info'
  Preparing metadata (pyproject.toml): finished with status 'done'
Building wheels for collected packages: dask
  Building wheel for dask (pyproject.toml): started
  Running command Building wheel for dask (pyproject.toml)
  /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated
  !!

          ********************************************************************************
          Please use a simple string containing a SPDX expression for `project.license`. You can also use `project.license-files`. (Both options available on setuptools>=77.0.0).

          By 2026-Feb-18, you need to update your project and remove deprecated calls
          or your builds will no longer be supported.

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
          ********************************************************************************

  !!
    corresp(dist, value, root_dir)
  /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:55: SetuptoolsDeprecationWarning: 'tool.setuptools.license-files' is deprecated in favor of 'project.license-files' (available on setuptools>=77.0.0).
  !!

          ********************************************************************************

          By 2026-Feb-18, you need to update your project and remove deprecated calls
          or your builds will no longer be supported.

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license-files for details.
          ********************************************************************************

  !!
    _apply_tool_table(dist, config, filename)
  /usr/lib/python3.14/site-packages/setuptools/config/_apply_pyprojecttoml.py:61: SetuptoolsDeprecationWarning: License classifiers are deprecated.
  !!

          ********************************************************************************
          Please consider removing the following classifiers in favor of a SPDX license expression:

          License :: OSI Approved :: BSD License

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
          ********************************************************************************

  !!
    dist._finalize_license_expression()
  /usr/lib/python3.14/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated.
  !!

          ********************************************************************************
          Please consider removing the following classifiers in favor of a SPDX license expression:

          License :: OSI Approved :: BSD License

          See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details.
          ********************************************************************************

  !!
    self._finalize_license_expression()
  running bdist_wheel
  running build
  running build_py
  creating build/lib/dask
  copying dask/utils_test.py -> build/lib/dask
  copying dask/utils.py -> build/lib/dask
  copying dask/typing.py -> build/lib/dask
  copying dask/tokenize.py -> build/lib/dask
  copying dask/threaded.py -> build/lib/dask
  copying dask/task_spec.py -> build/lib/dask
  copying dask/system.py -> build/lib/dask
  copying dask/sizeof.py -> build/lib/dask
  copying dask/rewrite.py -> build/lib/dask
  copying dask/order.py -> build/lib/dask
  copying dask/optimization.py -> build/lib/dask
  copying dask/multiprocessing.py -> build/lib/dask
  copying dask/ml.py -> build/lib/dask
  copying dask/local.py -> build/lib/dask
  copying dask/layers.py -> build/lib/dask
  copying dask/highlevelgraph.py -> build/lib/dask
  copying dask/hashing.py -> build/lib/dask
  copying dask/graph_manipulation.py -> build/lib/dask
  copying dask/dot.py -> build/lib/dask
  copying dask/distributed.py -> build/lib/dask
  copying dask/delayed.py -> build/lib/dask
  copying dask/datasets.py -> build/lib/dask
  copying dask/core.py -> build/lib/dask
  copying dask/context.py -> build/lib/dask
  copying dask/conftest.py -> build/lib/dask
  copying dask/config.py -> build/lib/dask
  copying dask/cli.py -> build/lib/dask
  copying dask/callbacks.py -> build/lib/dask
  copying dask/cache.py -> build/lib/dask
  copying dask/blockwise.py -> build/lib/dask
  copying dask/base.py -> build/lib/dask
  copying dask/backends.py -> build/lib/dask
  copying dask/_version.py -> build/lib/dask
  copying dask/_task_spec.py -> build/lib/dask
  copying dask/_expr.py -> build/lib/dask
  copying dask/_dispatch.py -> build/lib/dask
  copying dask/_compatibility.py -> build/lib/dask
  copying dask/_collections.py -> build/lib/dask
  copying dask/__main__.py -> build/lib/dask
  copying dask/__init__.py -> build/lib/dask
  creating build/lib/dask/widgets
  copying dask/widgets/widgets.py -> build/lib/dask/widgets
  copying dask/widgets/__init__.py -> build/lib/dask/widgets
  creating build/lib/dask/tests
  copying dask/tests/warning_aliases.py -> build/lib/dask/tests
  copying dask/tests/test_utils_test.py -> build/lib/dask/tests
  copying dask/tests/test_utils.py -> build/lib/dask/tests
  copying dask/tests/test_typing.py -> build/lib/dask/tests
  copying dask/tests/test_traceback.py -> build/lib/dask/tests
  copying dask/tests/test_tokenize.py -> build/lib/dask/tests
  copying dask/tests/test_threaded.py -> build/lib/dask/tests
  copying dask/tests/test_task_spec.py -> build/lib/dask/tests
  copying dask/tests/test_system.py -> build/lib/dask/tests
  copying dask/tests/test_spark_compat.py -> build/lib/dask/tests
  copying dask/tests/test_sizeof.py -> build/lib/dask/tests
  copying dask/tests/test_rewrite.py -> build/lib/dask/tests
  copying dask/tests/test_order.py -> build/lib/dask/tests
  copying dask/tests/test_optimization.py -> build/lib/dask/tests
  copying dask/tests/test_multiprocessing.py -> build/lib/dask/tests
  copying dask/tests/test_ml.py -> build/lib/dask/tests
  copying dask/tests/test_local.py -> build/lib/dask/tests
  copying dask/tests/test_layers.py -> build/lib/dask/tests
  copying dask/tests/test_imports.py -> build/lib/dask/tests
  copying dask/tests/test_hlgexpr.py -> build/lib/dask/tests
  copying dask/tests/test_highgraph.py -> build/lib/dask/tests
  copying dask/tests/test_hashing.py -> build/lib/dask/tests
  copying dask/tests/test_graph_manipulation.py -> build/lib/dask/tests
  copying dask/tests/test_expr.py -> build/lib/dask/tests
  copying dask/tests/test_dot.py -> build/lib/dask/tests
  copying dask/tests/test_docs.py -> build/lib/dask/tests
  copying dask/tests/test_distributed.py -> build/lib/dask/tests
  copying dask/tests/test_delayed.py -> build/lib/dask/tests
  copying dask/tests/test_datasets.py -> build/lib/dask/tests
  copying dask/tests/test_core.py -> build/lib/dask/tests
  copying dask/tests/test_context.py -> build/lib/dask/tests
  copying dask/tests/test_config.py -> build/lib/dask/tests
  copying dask/tests/test_compatibility.py -> build/lib/dask/tests
  copying dask/tests/test_cli.py -> build/lib/dask/tests
  copying dask/tests/test_ci.py -> build/lib/dask/tests
  copying dask/tests/test_callbacks.py -> build/lib/dask/tests
  copying dask/tests/test_cache.py -> build/lib/dask/tests
  copying dask/tests/test_base.py -> build/lib/dask/tests
  copying dask/tests/test_backends.py -> build/lib/dask/tests
  copying dask/tests/__init__.py -> build/lib/dask/tests
  creating build/lib/dask/diagnostics
  copying dask/diagnostics/progress.py -> build/lib/dask/diagnostics
  copying dask/diagnostics/profile_visualize.py -> build/lib/dask/diagnostics
  copying dask/diagnostics/profile.py -> build/lib/dask/diagnostics
  copying dask/diagnostics/__init__.py -> build/lib/dask/diagnostics
  creating build/lib/dask/dataframe
  copying dask/dataframe/utils.py -> build/lib/dask/dataframe
  copying dask/dataframe/shuffle.py -> build/lib/dask/dataframe
  copying dask/dataframe/rolling.py -> build/lib/dask/dataframe
  copying dask/dataframe/partitionquantiles.py -> build/lib/dask/dataframe
  copying dask/dataframe/multi.py -> build/lib/dask/dataframe
  copying dask/dataframe/methods.py -> build/lib/dask/dataframe
  copying dask/dataframe/indexing.py -> build/lib/dask/dataframe
  copying dask/dataframe/hyperloglog.py -> build/lib/dask/dataframe
  copying dask/dataframe/groupby.py -> build/lib/dask/dataframe
  copying dask/dataframe/extensions.py -> build/lib/dask/dataframe
  copying dask/dataframe/dispatch.py -> build/lib/dask/dataframe
  copying dask/dataframe/core.py -> build/lib/dask/dataframe
  copying dask/dataframe/categorical.py -> build/lib/dask/dataframe
  copying dask/dataframe/backends.py -> build/lib/dask/dataframe
  copying dask/dataframe/api.py -> build/lib/dask/dataframe
  copying dask/dataframe/accessor.py -> build/lib/dask/dataframe
  copying dask/dataframe/_pyarrow_compat.py -> build/lib/dask/dataframe
  copying dask/dataframe/_pyarrow.py -> build/lib/dask/dataframe
  copying dask/dataframe/_dtypes.py -> build/lib/dask/dataframe
  copying dask/dataframe/_compat.py -> build/lib/dask/dataframe
  copying dask/dataframe/__init__.py -> build/lib/dask/dataframe
  creating build/lib/dask/bytes
  copying dask/bytes/utils.py -> build/lib/dask/bytes
  copying dask/bytes/core.py -> build/lib/dask/bytes
  copying dask/bytes/__init__.py -> build/lib/dask/bytes
  creating build/lib/dask/bag
  copying dask/bag/utils.py -> build/lib/dask/bag
  copying dask/bag/text.py -> build/lib/dask/bag
  copying dask/bag/random.py -> build/lib/dask/bag
  copying dask/bag/core.py -> build/lib/dask/bag
  copying dask/bag/chunk.py -> build/lib/dask/bag
  copying dask/bag/avro.py -> build/lib/dask/bag
  copying dask/bag/__init__.py -> build/lib/dask/bag
  creating build/lib/dask/array
  copying dask/array/wrap.py -> build/lib/dask/array
  copying dask/array/utils.py -> build/lib/dask/array
  copying dask/array/ufunc.py -> build/lib/dask/array
  copying dask/array/tiledb_io.py -> build/lib/dask/array
  copying dask/array/svg.py -> build/lib/dask/array
  copying dask/array/stats.py -> build/lib/dask/array
  copying dask/array/slicing.py -> build/lib/dask/array
  copying dask/array/routines.py -> build/lib/dask/array
  copying dask/array/reshape.py -> build/lib/dask/array
  copying dask/array/reductions.py -> build/lib/dask/array
  copying dask/array/rechunk.py -> build/lib/dask/array
  copying dask/array/random.py -> build/lib/dask/array
  copying dask/array/percentile.py -> build/lib/dask/array
  copying dask/array/overlap.py -> build/lib/dask/array
  copying dask/array/optimization.py -> build/lib/dask/array
  copying dask/array/numpy_compat.py -> build/lib/dask/array
  copying dask/array/ma.py -> build/lib/dask/array
  copying dask/array/linalg.py -> build/lib/dask/array
  copying dask/array/image.py -> build/lib/dask/array
  copying dask/array/gufunc.py -> build/lib/dask/array
  copying dask/array/fft.py -> build/lib/dask/array
  copying dask/array/einsumfuncs.py -> build/lib/dask/array
  copying dask/array/dispatch.py -> build/lib/dask/array
  copying dask/array/cupy_entry_point.py -> build/lib/dask/array
  copying dask/array/creation.py -> build/lib/dask/array
  copying dask/array/core.py -> build/lib/dask/array
  copying dask/array/chunk_types.py -> build/lib/dask/array
  copying dask/array/chunk.py -> build/lib/dask/array
  copying dask/array/blockwise.py -> build/lib/dask/array
  copying dask/array/backends.py -> build/lib/dask/array
  copying dask/array/api.py -> build/lib/dask/array
  copying dask/array/_shuffle.py -> build/lib/dask/array
  copying dask/array/_reductions_generic.py -> build/lib/dask/array
  copying dask/array/__init__.py -> build/lib/dask/array
  creating build/lib/dask/diagnostics/tests
  copying dask/diagnostics/tests/test_progress.py -> build/lib/dask/diagnostics/tests
  copying dask/diagnostics/tests/test_profiler.py -> build/lib/dask/diagnostics/tests
  copying dask/diagnostics/tests/__init__.py -> build/lib/dask/diagnostics/tests
  creating build/lib/dask/dataframe/tseries
  copying dask/dataframe/tseries/resample.py -> build/lib/dask/dataframe/tseries
  copying dask/dataframe/tseries/__init__.py -> build/lib/dask/dataframe/tseries
  creating build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_utils_dataframe.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_ufunc.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_shuffle.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_rolling.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_reshape.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_pyarrow_compat.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_pyarrow.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_numeric.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_multi.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_methods.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_merge_column_and_index.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_indexing.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_hyperloglog.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_hashing.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_groupby.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_extensions.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_dataframe.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_categorical.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_boolean.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_arithmetics_reduction.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_api.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/test_accessors.py -> build/lib/dask/dataframe/tests
  copying dask/dataframe/tests/__init__.py -> build/lib/dask/dataframe/tests
  creating build/lib/dask/dataframe/io
  copying dask/dataframe/io/utils.py -> build/lib/dask/dataframe/io
  copying dask/dataframe/io/sql.py -> build/lib/dask/dataframe/io
  copying dask/dataframe/io/json.py -> build/lib/dask/dataframe/io
  copying dask/dataframe/io/io.py -> build/lib/dask/dataframe/io
  copying dask/dataframe/io/hdf.py -> build/lib/dask/dataframe/io
  copying dask/dataframe/io/demo.py -> build/lib/dask/dataframe/io
  copying dask/dataframe/io/csv.py -> build/lib/dask/dataframe/io
  copying dask/dataframe/io/__init__.py -> build/lib/dask/dataframe/io
  creating build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/datasets.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_version.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_util.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_str_accessor.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_shuffle.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_rolling.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_repartition.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_reductions.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_quantiles.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_quantile.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_merge_asof.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_merge.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_interchange.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_indexing.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_groupby.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_expr.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_dummies.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_describe.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_datetime.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_cumulative.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_concat.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_collection.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_categorical.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_backends.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/_accessor.py -> build/lib/dask/dataframe/dask_expr
  copying dask/dataframe/dask_expr/__init__.py -> build/lib/dask/dataframe/dask_expr
  creating build/lib/dask/dataframe/tseries/tests
  copying dask/dataframe/tseries/tests/test_resample_expr.py -> build/lib/dask/dataframe/tseries/tests
  copying dask/dataframe/tseries/tests/test_resample.py -> build/lib/dask/dataframe/tseries/tests
  copying dask/dataframe/tseries/tests/__init__.py -> build/lib/dask/dataframe/tseries/tests
  creating build/lib/dask/dataframe/io/tests
  copying dask/dataframe/io/tests/test_sql.py -> build/lib/dask/dataframe/io/tests
  copying dask/dataframe/io/tests/test_parquet.py -> build/lib/dask/dataframe/io/tests
  copying dask/dataframe/io/tests/test_orc.py -> build/lib/dask/dataframe/io/tests
  copying dask/dataframe/io/tests/test_json.py -> build/lib/dask/dataframe/io/tests
  copying dask/dataframe/io/tests/test_io.py -> build/lib/dask/dataframe/io/tests
  copying dask/dataframe/io/tests/test_hdf.py -> build/lib/dask/dataframe/io/tests
  copying dask/dataframe/io/tests/test_demo.py -> build/lib/dask/dataframe/io/tests
  copying dask/dataframe/io/tests/test_csv.py -> build/lib/dask/dataframe/io/tests
  copying dask/dataframe/io/tests/__init__.py -> build/lib/dask/dataframe/io/tests
  creating build/lib/dask/dataframe/io/parquet
  copying dask/dataframe/io/parquet/utils.py -> build/lib/dask/dataframe/io/parquet
  copying dask/dataframe/io/parquet/core.py -> build/lib/dask/dataframe/io/parquet
  copying dask/dataframe/io/parquet/arrow.py -> build/lib/dask/dataframe/io/parquet
  copying dask/dataframe/io/parquet/__init__.py -> build/lib/dask/dataframe/io/parquet
  creating build/lib/dask/dataframe/io/orc
  copying dask/dataframe/io/orc/utils.py -> build/lib/dask/dataframe/io/orc
  copying dask/dataframe/io/orc/core.py -> build/lib/dask/dataframe/io/orc
  copying dask/dataframe/io/orc/arrow.py -> build/lib/dask/dataframe/io/orc
  copying dask/dataframe/io/orc/__init__.py -> build/lib/dask/dataframe/io/orc
  creating build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_ufunc.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_string_accessor.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_shuffle.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_rolling.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_reshape.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_repartition.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_reductions.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_quantiles.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_predicate_pushdown.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_merge_asof.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_merge.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_interchange.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_indexing.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_groupby.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_fusion.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_format.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_dummies.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_distributed.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_diagnostics.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_describe.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_datetime.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_datasets.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_cumulative.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_core.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_concat.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_collection.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_categorical.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/test_align_partitions.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/_util.py -> build/lib/dask/dataframe/dask_expr/tests
  copying dask/dataframe/dask_expr/tests/__init__.py -> build/lib/dask/dataframe/dask_expr/tests
  creating build/lib/dask/dataframe/dask_expr/io
  copying dask/dataframe/dask_expr/io/records.py -> build/lib/dask/dataframe/dask_expr/io
  copying dask/dataframe/dask_expr/io/parquet.py -> build/lib/dask/dataframe/dask_expr/io
  copying dask/dataframe/dask_expr/io/io.py -> build/lib/dask/dataframe/dask_expr/io
  copying dask/dataframe/dask_expr/io/bag.py -> build/lib/dask/dataframe/dask_expr/io
  copying dask/dataframe/dask_expr/io/_delayed.py -> build/lib/dask/dataframe/dask_expr/io
  copying dask/dataframe/dask_expr/io/__init__.py -> build/lib/dask/dataframe/dask_expr/io
  creating build/lib/dask/dataframe/dask_expr/diagnostics
  copying dask/dataframe/dask_expr/diagnostics/_explain.py -> build/lib/dask/dataframe/dask_expr/diagnostics
  copying dask/dataframe/dask_expr/diagnostics/_analyze_plugin.py -> build/lib/dask/dataframe/dask_expr/diagnostics
  copying dask/dataframe/dask_expr/diagnostics/_analyze.py -> build/lib/dask/dataframe/dask_expr/diagnostics
  copying dask/dataframe/dask_expr/diagnostics/__init__.py -> build/lib/dask/dataframe/dask_expr/diagnostics
  creating build/lib/dask/dataframe/dask_expr/io/tests
  copying dask/dataframe/dask_expr/io/tests/test_sql.py -> build/lib/dask/dataframe/dask_expr/io/tests
  copying dask/dataframe/dask_expr/io/tests/test_parquet.py -> build/lib/dask/dataframe/dask_expr/io/tests
  copying dask/dataframe/dask_expr/io/tests/test_io.py -> build/lib/dask/dataframe/dask_expr/io/tests
  copying dask/dataframe/dask_expr/io/tests/test_from_pandas.py -> build/lib/dask/dataframe/dask_expr/io/tests
  copying dask/dataframe/dask_expr/io/tests/test_distributed.py -> build/lib/dask/dataframe/dask_expr/io/tests
  copying dask/dataframe/dask_expr/io/tests/test_delayed.py -> build/lib/dask/dataframe/dask_expr/io/tests
  copying dask/dataframe/dask_expr/io/tests/__init__.py -> build/lib/dask/dataframe/dask_expr/io/tests
  creating build/lib/dask/bytes/tests
  copying dask/bytes/tests/test_s3.py -> build/lib/dask/bytes/tests
  copying dask/bytes/tests/test_local.py -> build/lib/dask/bytes/tests
  copying dask/bytes/tests/test_http.py -> build/lib/dask/bytes/tests
  copying dask/bytes/tests/test_compression.py -> build/lib/dask/bytes/tests
  copying dask/bytes/tests/test_bytes_utils.py -> build/lib/dask/bytes/tests
  copying dask/bytes/tests/__init__.py -> build/lib/dask/bytes/tests
  creating build/lib/dask/bag/tests
  copying dask/bag/tests/test_text.py -> build/lib/dask/bag/tests
  copying dask/bag/tests/test_random.py -> build/lib/dask/bag/tests
  copying dask/bag/tests/test_bag.py -> build/lib/dask/bag/tests
  copying dask/bag/tests/test_avro.py -> build/lib/dask/bag/tests
  copying dask/bag/tests/__init__.py -> build/lib/dask/bag/tests
  creating build/lib/dask/array/tests
  copying dask/array/tests/test_array_core.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_xarray.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_wrap.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_ufunc.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_testing.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_svg.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_stats.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_sparse.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_slicing.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_shuffle.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_routines.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_reshape.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_reductions.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_rechunk.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_random.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_percentiles.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_overlap.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_optimization.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_numpy_compat.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_masked.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_map_blocks.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_linalg.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_image.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_gufunc.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_fft.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_dispatch.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_cupy_sparse.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_cupy_slicing.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_cupy_routines.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_cupy_reductions.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_cupy_random.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_cupy_percentile.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_cupy_overlap.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_cupy_linalg.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_cupy_gufunc.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_cupy_creation.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_cupy_core.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_creation.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_chunk.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_atop.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_array_utils.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_array_function.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_array_expr_incompatible.py -> build/lib/dask/array/tests
  copying dask/array/tests/test_api.py -> build/lib/dask/array/tests
  copying dask/array/tests/__init__.py -> build/lib/dask/array/tests
  creating build/lib/dask/array/lib
  copying dask/array/lib/stride_tricks.py -> build/lib/dask/array/lib
  copying dask/array/lib/__init__.py -> build/lib/dask/array/lib
  creating build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/random.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_utils.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_ufunc.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_slicing.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_shuffle.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_reductions.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_rechunk.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_overlap.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_map_blocks.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_io.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_gufunc.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_expr.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_creation.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_collection.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_blockwise.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/_backends.py -> build/lib/dask/array/_array_expr
  copying dask/array/_array_expr/__init__.py -> build/lib/dask/array/_array_expr
  creating build/lib/dask/array/_array_expr/tests
  copying dask/array/_array_expr/tests/test_collection.py -> build/lib/dask/array/_array_expr/tests
  copying dask/array/_array_expr/tests/__init__.py -> build/lib/dask/array/_array_expr/tests
  running egg_info
  writing dask.egg-info/PKG-INFO
  writing dependency_links to dask.egg-info/dependency_links.txt
  writing entry points to dask.egg-info/entry_points.txt
  writing requirements to dask.egg-info/requires.txt
  writing top-level names to dask.egg-info/top_level.txt
  reading manifest file 'dask.egg-info/SOURCES.txt'
  reading manifest template 'MANIFEST.in'
  warning: no files found matching 'versioneer.py'
  adding license file 'LICENSE.txt'
  adding license file 'dask/array/NUMPY_LICENSE.txt'
  writing manifest file 'dask.egg-info/SOURCES.txt'
  /usr/lib/python3.14/site-packages/setuptools/command/build_py.py:212: _Warning: Package 'dask.widgets.templates' is absent from the `packages` configuration.
  !!

          ********************************************************************************
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'dask.widgets.templates' as an importable package[^1],
          but it is absent from setuptools' `packages` configuration.

          This leads to an ambiguous overall configuration. If you want to distribute this
          package, please make sure that 'dask.widgets.templates' is explicitly added
          to the `packages` configuration field.

          Alternatively, you can also rely on setuptools' discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).

          You can read more about "package discovery" on setuptools documentation page:

          - https://setuptools.pypa.io/en/latest/userguide/package_discovery.html

          If you don't want 'dask.widgets.templates' to be distributed and are
          already explicitly excluding 'dask.widgets.templates' via
          `find_namespace_packages(...)/find_namespace` or `find_packages(...)/find`,
          you can try to use `exclude_package_data`, or `include-package-data=False` in
          combination with a more fine grained `package-data` configuration.

          You can read more about "package data files" on setuptools documentation page:

          - https://setuptools.pypa.io/en/latest/userguide/datafiles.html


          [^1]: For Python, any directory (with suitable naming) can be imported,
                even if it does not contain any `.py` files.
                On the other hand, currently there is no concept of package data
                directory, all directories are treated like packages.
          ********************************************************************************

  !!
    check.warn(importable)
  /usr/lib/python3.14/site-packages/setuptools/command/build_py.py:212: _Warning: Package 'dask.widgets.tests' is absent from the `packages` configuration.
  !!

          ********************************************************************************
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'dask.widgets.tests' as an importable package[^1],
          but it is absent from setuptools' `packages` configuration.

          This leads to an ambiguous overall configuration. If you want to distribute this
          package, please make sure that 'dask.widgets.tests' is explicitly added
          to the `packages` configuration field.

          Alternatively, you can also rely on setuptools' discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).

          You can read more about "package discovery" on setuptools documentation page:

          - https://setuptools.pypa.io/en/latest/userguide/package_discovery.html

          If you don't want 'dask.widgets.tests' to be distributed and are
          already explicitly excluding 'dask.widgets.tests' via
          `find_namespace_packages(...)/find_namespace` or `find_packages(...)/find`,
          you can try to use `exclude_package_data`, or `include-package-data=False` in
          combination with a more fine grained `package-data` configuration.

          You can read more about "package data files" on setuptools documentation page:

          - https://setuptools.pypa.io/en/latest/userguide/datafiles.html


          [^1]: For Python, any directory (with suitable naming) can be imported,
                even if it does not contain any `.py` files.
                On the other hand, currently there is no concept of package data
                directory, all directories are treated like packages.
          ********************************************************************************

  !!
    check.warn(importable)
  /usr/lib/python3.14/site-packages/setuptools/command/build_py.py:212: _Warning: Package 'dask.widgets.tests.templates' is absent from the `packages` configuration.
  !!

          ********************************************************************************
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'dask.widgets.tests.templates' as an importable package[^1],
          but it is absent from setuptools' `packages` configuration.

          This leads to an ambiguous overall configuration. If you want to distribute this
          package, please make sure that 'dask.widgets.tests.templates' is explicitly added
          to the `packages` configuration field.

          Alternatively, you can also rely on setuptools' discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).

          You can read more about "package discovery" on setuptools documentation page:

          - https://setuptools.pypa.io/en/latest/userguide/package_discovery.html

          If you don't want 'dask.widgets.tests.templates' to be distributed and are
          already explicitly excluding 'dask.widgets.tests.templates' via
          `find_namespace_packages(...)/find_namespace` or `find_packages(...)/find`,
          you can try to use `exclude_package_data`, or `include-package-data=False` in
          combination with a more fine grained `package-data` configuration.

          You can read more about "package data files" on setuptools documentation page:

          - https://setuptools.pypa.io/en/latest/userguide/datafiles.html


          [^1]: For Python, any directory (with suitable naming) can be imported,
                even if it does not contain any `.py` files.
                On the other hand, currently there is no concept of package data
                directory, all directories are treated like packages.
          ********************************************************************************

  !!
    check.warn(importable)
  copying dask/dask-schema.yaml -> build/lib/dask
  copying dask/dask.yaml -> build/lib/dask
  copying dask/py.typed -> build/lib/dask
  creating build/lib/dask/widgets/templates
  copying dask/widgets/templates/array.html.j2 -> build/lib/dask/widgets/templates
  copying dask/widgets/templates/dataframe.html.j2 -> build/lib/dask/widgets/templates
  copying dask/widgets/templates/highlevelgraph.html.j2 -> build/lib/dask/widgets/templates
  copying dask/widgets/templates/highlevelgraph_layer.html.j2 -> build/lib/dask/widgets/templates
  creating build/lib/dask/widgets/tests
  copying dask/widgets/tests/test_widgets.py -> build/lib/dask/widgets/tests
  creating build/lib/dask/widgets/tests/templates
  copying dask/widgets/tests/templates/bytes.html.j2 -> build/lib/dask/widgets/tests/templates
  copying dask/widgets/tests/templates/custom_filter.html.j2 -> build/lib/dask/widgets/tests/templates
  copying dask/widgets/tests/templates/example.html.j2 -> build/lib/dask/widgets/tests/templates
  copying dask/array/NUMPY_LICENSE.txt -> build/lib/dask/array
  UPDATING build/lib/dask/_version.py
  set build/lib/dask/_version.py to '2025.4.1'
  installing to build/bdist.linux-x86_64/wheel
  running install
  running install_lib
  creating build/bdist.linux-x86_64/wheel
  creating build/bdist.linux-x86_64/wheel/dask
  copying build/lib/dask/py.typed -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/dask.yaml -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/dask-schema.yaml -> build/bdist.linux-x86_64/wheel/./dask
  creating build/bdist.linux-x86_64/wheel/dask/array
  copying build/lib/dask/array/NUMPY_LICENSE.txt -> build/bdist.linux-x86_64/wheel/./dask/array
  creating build/bdist.linux-x86_64/wheel/dask/array/_array_expr
  creating build/bdist.linux-x86_64/wheel/dask/array/_array_expr/tests
  copying build/lib/dask/array/_array_expr/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr/tests
  copying build/lib/dask/array/_array_expr/tests/test_collection.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr/tests
  copying build/lib/dask/array/_array_expr/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_backends.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_blockwise.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_collection.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_creation.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_expr.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_gufunc.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_io.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_map_blocks.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_overlap.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_rechunk.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_reductions.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_shuffle.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_slicing.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_ufunc.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/_utils.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  copying build/lib/dask/array/_array_expr/random.py -> build/bdist.linux-x86_64/wheel/./dask/array/_array_expr
  creating build/bdist.linux-x86_64/wheel/dask/array/lib
  copying build/lib/dask/array/lib/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/array/lib
  copying build/lib/dask/array/lib/stride_tricks.py -> build/bdist.linux-x86_64/wheel/./dask/array/lib
  creating build/bdist.linux-x86_64/wheel/dask/array/tests
  copying build/lib/dask/array/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_api.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_array_expr_incompatible.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_array_function.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_array_utils.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_atop.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_chunk.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_creation.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_cupy_core.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_cupy_creation.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_cupy_gufunc.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_cupy_linalg.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_cupy_overlap.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_cupy_percentile.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_cupy_random.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_cupy_reductions.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_cupy_routines.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_cupy_slicing.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_cupy_sparse.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_dispatch.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_fft.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_gufunc.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_image.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_linalg.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_map_blocks.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_masked.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_numpy_compat.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_optimization.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_overlap.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_percentiles.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_random.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_rechunk.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_reductions.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_reshape.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_routines.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_shuffle.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_slicing.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_sparse.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_stats.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_svg.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_testing.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_ufunc.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_wrap.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_xarray.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/tests/test_array_core.py -> build/bdist.linux-x86_64/wheel/./dask/array/tests
  copying build/lib/dask/array/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/_reductions_generic.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/_shuffle.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/api.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/backends.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/blockwise.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/chunk.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/chunk_types.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/core.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/creation.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/cupy_entry_point.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/dispatch.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/einsumfuncs.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/fft.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/gufunc.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/image.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/linalg.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/ma.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/numpy_compat.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/optimization.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/overlap.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/percentile.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/random.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/rechunk.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/reductions.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/reshape.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/routines.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/slicing.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/stats.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/svg.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/tiledb_io.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/ufunc.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/utils.py -> build/bdist.linux-x86_64/wheel/./dask/array
  copying build/lib/dask/array/wrap.py -> build/bdist.linux-x86_64/wheel/./dask/array
  creating build/bdist.linux-x86_64/wheel/dask/bag
  creating build/bdist.linux-x86_64/wheel/dask/bag/tests
  copying build/lib/dask/bag/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/bag/tests
  copying build/lib/dask/bag/tests/test_avro.py -> build/bdist.linux-x86_64/wheel/./dask/bag/tests
  copying build/lib/dask/bag/tests/test_bag.py -> build/bdist.linux-x86_64/wheel/./dask/bag/tests
  copying build/lib/dask/bag/tests/test_random.py -> build/bdist.linux-x86_64/wheel/./dask/bag/tests
  copying build/lib/dask/bag/tests/test_text.py -> build/bdist.linux-x86_64/wheel/./dask/bag/tests
  copying build/lib/dask/bag/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/bag
  copying build/lib/dask/bag/avro.py -> build/bdist.linux-x86_64/wheel/./dask/bag
  copying build/lib/dask/bag/chunk.py -> build/bdist.linux-x86_64/wheel/./dask/bag
  copying build/lib/dask/bag/core.py -> build/bdist.linux-x86_64/wheel/./dask/bag
  copying build/lib/dask/bag/random.py -> build/bdist.linux-x86_64/wheel/./dask/bag
  copying build/lib/dask/bag/text.py -> build/bdist.linux-x86_64/wheel/./dask/bag
  copying build/lib/dask/bag/utils.py -> build/bdist.linux-x86_64/wheel/./dask/bag
  creating build/bdist.linux-x86_64/wheel/dask/bytes
  creating build/bdist.linux-x86_64/wheel/dask/bytes/tests
  copying build/lib/dask/bytes/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/bytes/tests
  copying build/lib/dask/bytes/tests/test_bytes_utils.py -> build/bdist.linux-x86_64/wheel/./dask/bytes/tests
  copying build/lib/dask/bytes/tests/test_compression.py -> build/bdist.linux-x86_64/wheel/./dask/bytes/tests
  copying build/lib/dask/bytes/tests/test_http.py -> build/bdist.linux-x86_64/wheel/./dask/bytes/tests
  copying build/lib/dask/bytes/tests/test_local.py -> build/bdist.linux-x86_64/wheel/./dask/bytes/tests
  copying build/lib/dask/bytes/tests/test_s3.py -> build/bdist.linux-x86_64/wheel/./dask/bytes/tests
  copying build/lib/dask/bytes/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/bytes
  copying build/lib/dask/bytes/core.py -> build/bdist.linux-x86_64/wheel/./dask/bytes
  copying build/lib/dask/bytes/utils.py -> build/bdist.linux-x86_64/wheel/./dask/bytes
  creating build/bdist.linux-x86_64/wheel/dask/dataframe
  creating build/bdist.linux-x86_64/wheel/dask/dataframe/dask_expr
  creating build/bdist.linux-x86_64/wheel/dask/dataframe/dask_expr/diagnostics
  copying build/lib/dask/dataframe/dask_expr/diagnostics/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/diagnostics
  copying build/lib/dask/dataframe/dask_expr/diagnostics/_analyze.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/diagnostics
  copying build/lib/dask/dataframe/dask_expr/diagnostics/_analyze_plugin.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/diagnostics
  copying build/lib/dask/dataframe/dask_expr/diagnostics/_explain.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/diagnostics
  creating build/bdist.linux-x86_64/wheel/dask/dataframe/dask_expr/io
  creating build/bdist.linux-x86_64/wheel/dask/dataframe/dask_expr/io/tests
  copying build/lib/dask/dataframe/dask_expr/io/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io/tests
  copying build/lib/dask/dataframe/dask_expr/io/tests/test_delayed.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io/tests
  copying build/lib/dask/dataframe/dask_expr/io/tests/test_distributed.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io/tests
  copying build/lib/dask/dataframe/dask_expr/io/tests/test_from_pandas.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io/tests
  copying build/lib/dask/dataframe/dask_expr/io/tests/test_io.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io/tests
  copying build/lib/dask/dataframe/dask_expr/io/tests/test_parquet.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io/tests
  copying build/lib/dask/dataframe/dask_expr/io/tests/test_sql.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io/tests
  copying build/lib/dask/dataframe/dask_expr/io/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io
  copying build/lib/dask/dataframe/dask_expr/io/_delayed.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io
  copying build/lib/dask/dataframe/dask_expr/io/bag.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io
  copying build/lib/dask/dataframe/dask_expr/io/io.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io
  copying build/lib/dask/dataframe/dask_expr/io/parquet.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io
  copying build/lib/dask/dataframe/dask_expr/io/records.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/io
  creating build/bdist.linux-x86_64/wheel/dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/_util.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_align_partitions.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_categorical.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_collection.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_concat.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_core.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_cumulative.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_datasets.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_datetime.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_describe.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_diagnostics.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_distributed.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_dummies.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_format.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_fusion.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_groupby.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
  copying build/lib/dask/dataframe/dask_expr/tests/test_indexing.py -> build/bdist.linux-x86_64/wheel/./dask/dataframe/dask_expr/tests
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  copying build/lib/dask/widgets/templates/array.html.j2 -> build/bdist.linux-x86_64/wheel/./dask/widgets/templates
  copying build/lib/dask/widgets/__init__.py -> build/bdist.linux-x86_64/wheel/./dask/widgets
  copying build/lib/dask/widgets/widgets.py -> build/bdist.linux-x86_64/wheel/./dask/widgets
  copying build/lib/dask/__init__.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/__main__.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/_collections.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/_compatibility.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/_dispatch.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/_expr.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/_task_spec.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/_version.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/backends.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/base.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/blockwise.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/cache.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/callbacks.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/cli.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/config.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/conftest.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/context.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/core.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/datasets.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/delayed.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/distributed.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/dot.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/graph_manipulation.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/hashing.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/highlevelgraph.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/layers.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/local.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/ml.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/multiprocessing.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/optimization.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/order.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/rewrite.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/sizeof.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/system.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/task_spec.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/threaded.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/tokenize.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/typing.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/utils.py -> build/bdist.linux-x86_64/wheel/./dask
  copying build/lib/dask/utils_test.py -> build/bdist.linux-x86_64/wheel/./dask
  running install_egg_info
  Copying dask.egg-info to build/bdist.linux-x86_64/wheel/./dask-2025.4.1-py3.14.egg-info
  running install_scripts
  creating build/bdist.linux-x86_64/wheel/dask-2025.4.1.dist-info/WHEEL
  creating '/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir/pip-wheel-2tg58v5z/.tmp-6ytdnkpg/dask-2025.4.1-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it
  adding 'dask/__init__.py'
  adding 'dask/__main__.py'
  adding 'dask/_collections.py'
  adding 'dask/_compatibility.py'
  adding 'dask/_dispatch.py'
  adding 'dask/_expr.py'
  adding 'dask/_task_spec.py'
  adding 'dask/_version.py'
  adding 'dask/backends.py'
  adding 'dask/base.py'
  adding 'dask/blockwise.py'
  adding 'dask/cache.py'
  adding 'dask/callbacks.py'
  adding 'dask/cli.py'
  adding 'dask/config.py'
  adding 'dask/conftest.py'
  adding 'dask/context.py'
  adding 'dask/core.py'
  adding 'dask/dask-schema.yaml'
  adding 'dask/dask.yaml'
  adding 'dask/datasets.py'
  adding 'dask/delayed.py'
  adding 'dask/distributed.py'
  adding 'dask/dot.py'
  adding 'dask/graph_manipulation.py'
  adding 'dask/hashing.py'
  adding 'dask/highlevelgraph.py'
  adding 'dask/layers.py'
  adding 'dask/local.py'
  adding 'dask/ml.py'
  adding 'dask/multiprocessing.py'
  adding 'dask/optimization.py'
  adding 'dask/order.py'
  adding 'dask/py.typed'
  adding 'dask/rewrite.py'
  adding 'dask/sizeof.py'
  adding 'dask/system.py'
  adding 'dask/task_spec.py'
  adding 'dask/threaded.py'
  adding 'dask/tokenize.py'
  adding 'dask/typing.py'
  adding 'dask/utils.py'
  adding 'dask/utils_test.py'
  adding 'dask/array/NUMPY_LICENSE.txt'
  adding 'dask/array/__init__.py'
  adding 'dask/array/_reductions_generic.py'
  adding 'dask/array/_shuffle.py'
  adding 'dask/array/api.py'
  adding 'dask/array/backends.py'
  adding 'dask/array/blockwise.py'
  adding 'dask/array/chunk.py'
  adding 'dask/array/chunk_types.py'
  adding 'dask/array/core.py'
  adding 'dask/array/creation.py'
  adding 'dask/array/cupy_entry_point.py'
  adding 'dask/array/dispatch.py'
  adding 'dask/array/einsumfuncs.py'
  adding 'dask/array/fft.py'
  adding 'dask/array/gufunc.py'
  adding 'dask/array/image.py'
  adding 'dask/array/linalg.py'
  adding 'dask/array/ma.py'
  adding 'dask/array/numpy_compat.py'
  adding 'dask/array/optimization.py'
  adding 'dask/array/overlap.py'
  adding 'dask/array/percentile.py'
  adding 'dask/array/random.py'
  adding 'dask/array/rechunk.py'
  adding 'dask/array/reductions.py'
  adding 'dask/array/reshape.py'
  adding 'dask/array/routines.py'
  adding 'dask/array/slicing.py'
  adding 'dask/array/stats.py'
  adding 'dask/array/svg.py'
  adding 'dask/array/tiledb_io.py'
  adding 'dask/array/ufunc.py'
  adding 'dask/array/utils.py'
  adding 'dask/array/wrap.py'
  adding 'dask/array/_array_expr/__init__.py'
  adding 'dask/array/_array_expr/_backends.py'
  adding 'dask/array/_array_expr/_blockwise.py'
  adding 'dask/array/_array_expr/_collection.py'
  adding 'dask/array/_array_expr/_creation.py'
  adding 'dask/array/_array_expr/_expr.py'
  adding 'dask/array/_array_expr/_gufunc.py'
  adding 'dask/array/_array_expr/_io.py'
  adding 'dask/array/_array_expr/_map_blocks.py'
  adding 'dask/array/_array_expr/_overlap.py'
  adding 'dask/array/_array_expr/_rechunk.py'
  adding 'dask/array/_array_expr/_reductions.py'
  adding 'dask/array/_array_expr/_shuffle.py'
  adding 'dask/array/_array_expr/_slicing.py'
  adding 'dask/array/_array_expr/_ufunc.py'
  adding 'dask/array/_array_expr/_utils.py'
  adding 'dask/array/_array_expr/random.py'
  adding 'dask/array/_array_expr/tests/__init__.py'
  adding 'dask/array/_array_expr/tests/test_collection.py'
  adding 'dask/array/lib/__init__.py'
  adding 'dask/array/lib/stride_tricks.py'
  adding 'dask/array/tests/__init__.py'
  adding 'dask/array/tests/test_api.py'
  adding 'dask/array/tests/test_array_core.py'
  adding 'dask/array/tests/test_array_expr_incompatible.py'
  adding 'dask/array/tests/test_array_function.py'
  adding 'dask/array/tests/test_array_utils.py'
  adding 'dask/array/tests/test_atop.py'
  adding 'dask/array/tests/test_chunk.py'
  adding 'dask/array/tests/test_creation.py'
  adding 'dask/array/tests/test_cupy_core.py'
  adding 'dask/array/tests/test_cupy_creation.py'
  adding 'dask/array/tests/test_cupy_gufunc.py'
  adding 'dask/array/tests/test_cupy_linalg.py'
  adding 'dask/array/tests/test_cupy_overlap.py'
  adding 'dask/array/tests/test_cupy_percentile.py'
  adding 'dask/array/tests/test_cupy_random.py'
  adding 'dask/array/tests/test_cupy_reductions.py'
  adding 'dask/array/tests/test_cupy_routines.py'
  adding 'dask/array/tests/test_cupy_slicing.py'
  adding 'dask/array/tests/test_cupy_sparse.py'
  adding 'dask/array/tests/test_dispatch.py'
  adding 'dask/array/tests/test_fft.py'
  adding 'dask/array/tests/test_gufunc.py'
  adding 'dask/array/tests/test_image.py'
  adding 'dask/array/tests/test_linalg.py'
  adding 'dask/array/tests/test_map_blocks.py'
  adding 'dask/array/tests/test_masked.py'
  adding 'dask/array/tests/test_numpy_compat.py'
  adding 'dask/array/tests/test_optimization.py'
  adding 'dask/array/tests/test_overlap.py'
  adding 'dask/array/tests/test_percentiles.py'
  adding 'dask/array/tests/test_random.py'
  adding 'dask/array/tests/test_rechunk.py'
  adding 'dask/array/tests/test_reductions.py'
  adding 'dask/array/tests/test_reshape.py'
  adding 'dask/array/tests/test_routines.py'
  adding 'dask/array/tests/test_shuffle.py'
  adding 'dask/array/tests/test_slicing.py'
  adding 'dask/array/tests/test_sparse.py'
  adding 'dask/array/tests/test_stats.py'
  adding 'dask/array/tests/test_svg.py'
  adding 'dask/array/tests/test_testing.py'
  adding 'dask/array/tests/test_ufunc.py'
  adding 'dask/array/tests/test_wrap.py'
  adding 'dask/array/tests/test_xarray.py'
  adding 'dask/bag/__init__.py'
  adding 'dask/bag/avro.py'
  adding 'dask/bag/chunk.py'
  adding 'dask/bag/core.py'
  adding 'dask/bag/random.py'
  adding 'dask/bag/text.py'
  adding 'dask/bag/utils.py'
  adding 'dask/bag/tests/__init__.py'
  adding 'dask/bag/tests/test_avro.py'
  adding 'dask/bag/tests/test_bag.py'
  adding 'dask/bag/tests/test_random.py'
  adding 'dask/bag/tests/test_text.py'
  adding 'dask/bytes/__init__.py'
  adding 'dask/bytes/core.py'
  adding 'dask/bytes/utils.py'
  adding 'dask/bytes/tests/__init__.py'
  adding 'dask/bytes/tests/test_bytes_utils.py'
  adding 'dask/bytes/tests/test_compression.py'
  adding 'dask/bytes/tests/test_http.py'
  adding 'dask/bytes/tests/test_local.py'
  adding 'dask/bytes/tests/test_s3.py'
  adding 'dask/dataframe/__init__.py'
  adding 'dask/dataframe/_compat.py'
  adding 'dask/dataframe/_dtypes.py'
  adding 'dask/dataframe/_pyarrow.py'
  adding 'dask/dataframe/_pyarrow_compat.py'
  adding 'dask/dataframe/accessor.py'
  adding 'dask/dataframe/api.py'
  adding 'dask/dataframe/backends.py'
  adding 'dask/dataframe/categorical.py'
  adding 'dask/dataframe/core.py'
  adding 'dask/dataframe/dispatch.py'
  adding 'dask/dataframe/extensions.py'
  adding 'dask/dataframe/groupby.py'
  adding 'dask/dataframe/hyperloglog.py'
  adding 'dask/dataframe/indexing.py'
  adding 'dask/dataframe/methods.py'
  adding 'dask/dataframe/multi.py'
  adding 'dask/dataframe/partitionquantiles.py'
  adding 'dask/dataframe/rolling.py'
  adding 'dask/dataframe/shuffle.py'
  adding 'dask/dataframe/utils.py'
  adding 'dask/dataframe/dask_expr/__init__.py'
  adding 'dask/dataframe/dask_expr/_accessor.py'
  adding 'dask/dataframe/dask_expr/_backends.py'
  adding 'dask/dataframe/dask_expr/_categorical.py'
  adding 'dask/dataframe/dask_expr/_collection.py'
  adding 'dask/dataframe/dask_expr/_concat.py'
  adding 'dask/dataframe/dask_expr/_cumulative.py'
  adding 'dask/dataframe/dask_expr/_datetime.py'
  adding 'dask/dataframe/dask_expr/_describe.py'
  adding 'dask/dataframe/dask_expr/_dummies.py'
  adding 'dask/dataframe/dask_expr/_expr.py'
  adding 'dask/dataframe/dask_expr/_groupby.py'
  adding 'dask/dataframe/dask_expr/_indexing.py'
  adding 'dask/dataframe/dask_expr/_interchange.py'
  adding 'dask/dataframe/dask_expr/_merge.py'
  adding 'dask/dataframe/dask_expr/_merge_asof.py'
  adding 'dask/dataframe/dask_expr/_quantile.py'
  adding 'dask/dataframe/dask_expr/_quantiles.py'
  adding 'dask/dataframe/dask_expr/_reductions.py'
  adding 'dask/dataframe/dask_expr/_repartition.py'
  adding 'dask/dataframe/dask_expr/_rolling.py'
  adding 'dask/dataframe/dask_expr/_shuffle.py'
  adding 'dask/dataframe/dask_expr/_str_accessor.py'
  adding 'dask/dataframe/dask_expr/_util.py'
  adding 'dask/dataframe/dask_expr/_version.py'
  adding 'dask/dataframe/dask_expr/datasets.py'
  adding 'dask/dataframe/dask_expr/diagnostics/__init__.py'
  adding 'dask/dataframe/dask_expr/diagnostics/_analyze.py'
  adding 'dask/dataframe/dask_expr/diagnostics/_analyze_plugin.py'
  adding 'dask/dataframe/dask_expr/diagnostics/_explain.py'
  adding 'dask/dataframe/dask_expr/io/__init__.py'
  adding 'dask/dataframe/dask_expr/io/_delayed.py'
  adding 'dask/dataframe/dask_expr/io/bag.py'
  adding 'dask/dataframe/dask_expr/io/io.py'
  adding 'dask/dataframe/dask_expr/io/parquet.py'
  adding 'dask/dataframe/dask_expr/io/records.py'
  adding 'dask/dataframe/dask_expr/io/tests/__init__.py'
  adding 'dask/dataframe/dask_expr/io/tests/test_delayed.py'
  adding 'dask/dataframe/dask_expr/io/tests/test_distributed.py'
  adding 'dask/dataframe/dask_expr/io/tests/test_from_pandas.py'
  adding 'dask/dataframe/dask_expr/io/tests/test_io.py'
  adding 'dask/dataframe/dask_expr/io/tests/test_parquet.py'
  adding 'dask/dataframe/dask_expr/io/tests/test_sql.py'
  adding 'dask/dataframe/dask_expr/tests/__init__.py'
  adding 'dask/dataframe/dask_expr/tests/_util.py'
  adding 'dask/dataframe/dask_expr/tests/test_align_partitions.py'
  adding 'dask/dataframe/dask_expr/tests/test_categorical.py'
  adding 'dask/dataframe/dask_expr/tests/test_collection.py'
  adding 'dask/dataframe/dask_expr/tests/test_concat.py'
  adding 'dask/dataframe/dask_expr/tests/test_core.py'
  adding 'dask/dataframe/dask_expr/tests/test_cumulative.py'
  adding 'dask/dataframe/dask_expr/tests/test_datasets.py'
  adding 'dask/dataframe/dask_expr/tests/test_datetime.py'
  adding 'dask/dataframe/dask_expr/tests/test_describe.py'
  adding 'dask/dataframe/dask_expr/tests/test_diagnostics.py'
  adding 'dask/dataframe/dask_expr/tests/test_distributed.py'
  adding 'dask/dataframe/dask_expr/tests/test_dummies.py'
  adding 'dask/dataframe/dask_expr/tests/test_format.py'
  adding 'dask/dataframe/dask_expr/tests/test_fusion.py'
  adding 'dask/dataframe/dask_expr/tests/test_groupby.py'
  adding 'dask/dataframe/dask_expr/tests/test_indexing.py'
  adding 'dask/dataframe/dask_expr/tests/test_interchange.py'
  adding 'dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py'
  adding 'dask/dataframe/dask_expr/tests/test_merge.py'
  adding 'dask/dataframe/dask_expr/tests/test_merge_asof.py'
  adding 'dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py'
  adding 'dask/dataframe/dask_expr/tests/test_predicate_pushdown.py'
  adding 'dask/dataframe/dask_expr/tests/test_quantiles.py'
  adding 'dask/dataframe/dask_expr/tests/test_reductions.py'
  adding 'dask/dataframe/dask_expr/tests/test_repartition.py'
  adding 'dask/dataframe/dask_expr/tests/test_reshape.py'
  adding 'dask/dataframe/dask_expr/tests/test_rolling.py'
  adding 'dask/dataframe/dask_expr/tests/test_shuffle.py'
  adding 'dask/dataframe/dask_expr/tests/test_string_accessor.py'
  adding 'dask/dataframe/dask_expr/tests/test_ufunc.py'
  adding 'dask/dataframe/io/__init__.py'
  adding 'dask/dataframe/io/csv.py'
  adding 'dask/dataframe/io/demo.py'
  adding 'dask/dataframe/io/hdf.py'
  adding 'dask/dataframe/io/io.py'
  adding 'dask/dataframe/io/json.py'
  adding 'dask/dataframe/io/sql.py'
  adding 'dask/dataframe/io/utils.py'
  adding 'dask/dataframe/io/orc/__init__.py'
  adding 'dask/dataframe/io/orc/arrow.py'
  adding 'dask/dataframe/io/orc/core.py'
  adding 'dask/dataframe/io/orc/utils.py'
  adding 'dask/dataframe/io/parquet/__init__.py'
  adding 'dask/dataframe/io/parquet/arrow.py'
  adding 'dask/dataframe/io/parquet/core.py'
  adding 'dask/dataframe/io/parquet/utils.py'
  adding 'dask/dataframe/io/tests/__init__.py'
  adding 'dask/dataframe/io/tests/test_csv.py'
  adding 'dask/dataframe/io/tests/test_demo.py'
  adding 'dask/dataframe/io/tests/test_hdf.py'
  adding 'dask/dataframe/io/tests/test_io.py'
  adding 'dask/dataframe/io/tests/test_json.py'
  adding 'dask/dataframe/io/tests/test_orc.py'
  adding 'dask/dataframe/io/tests/test_parquet.py'
  adding 'dask/dataframe/io/tests/test_sql.py'
  adding 'dask/dataframe/tests/__init__.py'
  adding 'dask/dataframe/tests/test_accessors.py'
  adding 'dask/dataframe/tests/test_api.py'
  adding 'dask/dataframe/tests/test_arithmetics_reduction.py'
  adding 'dask/dataframe/tests/test_boolean.py'
  adding 'dask/dataframe/tests/test_categorical.py'
  adding 'dask/dataframe/tests/test_dataframe.py'
  adding 'dask/dataframe/tests/test_extensions.py'
  adding 'dask/dataframe/tests/test_groupby.py'
  adding 'dask/dataframe/tests/test_hashing.py'
  adding 'dask/dataframe/tests/test_hyperloglog.py'
  adding 'dask/dataframe/tests/test_indexing.py'
  adding 'dask/dataframe/tests/test_merge_column_and_index.py'
  adding 'dask/dataframe/tests/test_methods.py'
  adding 'dask/dataframe/tests/test_multi.py'
  adding 'dask/dataframe/tests/test_numeric.py'
  adding 'dask/dataframe/tests/test_pyarrow.py'
  adding 'dask/dataframe/tests/test_pyarrow_compat.py'
  adding 'dask/dataframe/tests/test_reshape.py'
  adding 'dask/dataframe/tests/test_rolling.py'
  adding 'dask/dataframe/tests/test_shuffle.py'
  adding 'dask/dataframe/tests/test_ufunc.py'
  adding 'dask/dataframe/tests/test_utils_dataframe.py'
  adding 'dask/dataframe/tseries/__init__.py'
  adding 'dask/dataframe/tseries/resample.py'
  adding 'dask/dataframe/tseries/tests/__init__.py'
  adding 'dask/dataframe/tseries/tests/test_resample.py'
  adding 'dask/dataframe/tseries/tests/test_resample_expr.py'
  adding 'dask/diagnostics/__init__.py'
  adding 'dask/diagnostics/profile.py'
  adding 'dask/diagnostics/profile_visualize.py'
  adding 'dask/diagnostics/progress.py'
  adding 'dask/diagnostics/tests/__init__.py'
  adding 'dask/diagnostics/tests/test_profiler.py'
  adding 'dask/diagnostics/tests/test_progress.py'
  adding 'dask/tests/__init__.py'
  adding 'dask/tests/test_backends.py'
  adding 'dask/tests/test_base.py'
  adding 'dask/tests/test_cache.py'
  adding 'dask/tests/test_callbacks.py'
  adding 'dask/tests/test_ci.py'
  adding 'dask/tests/test_cli.py'
  adding 'dask/tests/test_compatibility.py'
  adding 'dask/tests/test_config.py'
  adding 'dask/tests/test_context.py'
  adding 'dask/tests/test_core.py'
  adding 'dask/tests/test_datasets.py'
  adding 'dask/tests/test_delayed.py'
  adding 'dask/tests/test_distributed.py'
  adding 'dask/tests/test_docs.py'
  adding 'dask/tests/test_dot.py'
  adding 'dask/tests/test_expr.py'
  adding 'dask/tests/test_graph_manipulation.py'
  adding 'dask/tests/test_hashing.py'
  adding 'dask/tests/test_highgraph.py'
  adding 'dask/tests/test_hlgexpr.py'
  adding 'dask/tests/test_imports.py'
  adding 'dask/tests/test_layers.py'
  adding 'dask/tests/test_local.py'
  adding 'dask/tests/test_ml.py'
  adding 'dask/tests/test_multiprocessing.py'
  adding 'dask/tests/test_optimization.py'
  adding 'dask/tests/test_order.py'
  adding 'dask/tests/test_rewrite.py'
  adding 'dask/tests/test_sizeof.py'
  adding 'dask/tests/test_spark_compat.py'
  adding 'dask/tests/test_system.py'
  adding 'dask/tests/test_task_spec.py'
  adding 'dask/tests/test_threaded.py'
  adding 'dask/tests/test_tokenize.py'
  adding 'dask/tests/test_traceback.py'
  adding 'dask/tests/test_typing.py'
  adding 'dask/tests/test_utils.py'
  adding 'dask/tests/test_utils_test.py'
  adding 'dask/tests/warning_aliases.py'
  adding 'dask/widgets/__init__.py'
  adding 'dask/widgets/widgets.py'
  adding 'dask/widgets/templates/array.html.j2'
  adding 'dask/widgets/templates/dataframe.html.j2'
  adding 'dask/widgets/templates/highlevelgraph.html.j2'
  adding 'dask/widgets/templates/highlevelgraph_layer.html.j2'
  adding 'dask/widgets/tests/test_widgets.py'
  adding 'dask/widgets/tests/templates/bytes.html.j2'
  adding 'dask/widgets/tests/templates/custom_filter.html.j2'
  adding 'dask/widgets/tests/templates/example.html.j2'
  adding 'dask-2025.4.1.dist-info/licenses/LICENSE.txt'
  adding 'dask-2025.4.1.dist-info/licenses/dask/array/NUMPY_LICENSE.txt'
  adding 'dask-2025.4.1.dist-info/METADATA'
  adding 'dask-2025.4.1.dist-info/WHEEL'
  adding 'dask-2025.4.1.dist-info/entry_points.txt'
  adding 'dask-2025.4.1.dist-info/top_level.txt'
  adding 'dask-2025.4.1.dist-info/RECORD'
  removing build/bdist.linux-x86_64/wheel
  Building wheel for dask (pyproject.toml): finished with status 'done'
  Created wheel for dask: filename=dask-2025.4.1-py3-none-any.whl size=1469014 sha256=cdc4ee45bdfc7b9173388c39c7f7ebcdb98d27500ad8eac82049e6e8ce97a38b
  Stored in directory: /builddir/.cache/pip/wheels/48/e7/e2/10e8f902fd4f916e241407e8217e733096f389567bbf164b4b
Successfully built dask
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%install): /bin/sh -e /var/tmp/rpm-tmp.xdldxU
+ umask 022
+ cd /builddir/build/BUILD/python-dask-2025.4.1-build
+ '[' /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT '!=' / ']'
+ rm -rf /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT
++ dirname /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT
+ mkdir -p /builddir/build/BUILD/python-dask-2025.4.1-build
+ mkdir /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ export RUSTFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd dask-2025.4.1
++ sed -E 's/([^-]+)-([^-]+)-.+\.whl/\1==\2/'
++ ls /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/pyproject-wheeldir/dask-2025.4.1-py3-none-any.whl
++ xargs basename --multiple
+ specifier=dask==2025.4.1
+ '[' -z dask==2025.4.1 ']'
+ TMPDIR=/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir
+ /usr/bin/python3 -m pip install --root /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT --prefix /usr --no-deps --disable-pip-version-check --progress-bar off --verbose --ignore-installed --no-warn-script-location --no-index --no-cache-dir --find-links /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/pyproject-wheeldir dask==2025.4.1
Using pip 25.1.1 from /usr/lib/python3.14/site-packages/pip (python 3.14)
Looking in links: /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/pyproject-wheeldir
Processing ./pyproject-wheeldir/dask-2025.4.1-py3-none-any.whl
Installing collected packages: dask
  Creating /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/bin
  changing mode of /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/bin/dask to 755
Successfully installed dask-2025.4.1
+ '[' -d /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/bin ']'
+ '[' -z sP ']'
+ shebang_flags=-kasP
+ /usr/bin/python3 -B /usr/lib/rpm/redhat/pathfix.py -pni /usr/bin/python3 -kasP /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/bin/dask
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/bin/dask: updating
+ rm -rfv /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/bin/__pycache__
+ rm -f /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-ghost-distinfo
+ site_dirs=()
+ '[' -d /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages ']'
+ site_dirs+=("/usr/lib/python3.14/site-packages")
+ '[' /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib64/python3.14/site-packages '!=' /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages ']'
+ '[' -d /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib64/python3.14/site-packages ']'
+ for site_dir in ${site_dirs[@]}
+ for distinfo in /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT$site_dir/*.dist-info
+ echo '%ghost /usr/lib/python3.14/site-packages/dask-2025.4.1.dist-info'
+ sed -i s/pip/rpm/ /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask-2025.4.1.dist-info/INSTALLER
+ PYTHONPATH=/usr/lib/rpm/redhat
+ /usr/bin/python3 -B /usr/lib/rpm/redhat/pyproject_preprocess_record.py --buildroot /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT --record /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask-2025.4.1.dist-info/RECORD --output /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-record
+ rm -fv /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask-2025.4.1.dist-info/RECORD
removed '/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask-2025.4.1.dist-info/RECORD'
+ rm -fv /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask-2025.4.1.dist-info/REQUESTED
removed '/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask-2025.4.1.dist-info/REQUESTED'
++ wc -l /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-ghost-distinfo
++ cut -f1 '-d '
+ lines=1
+ '[' 1 -ne 1 ']'
+ RPM_FILES_ESCAPE=4.19
+ /usr/bin/python3 /usr/lib/rpm/redhat/pyproject_save_files.py --output-files /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-files --output-modules /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-modules --buildroot /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT --sitelib /usr/lib/python3.14/site-packages --sitearch /usr/lib64/python3.14/site-packages --python-version 3.14 --pyproject-record /builddir/build/BUILD/python-dask-2025.4.1-build/python-dask-2025.4.1-3.fc43~bootstrap.x86_64-pyproject-record --prefix /usr -l dask
+ /usr/lib/rpm/check-buildroot
+ /usr/lib/rpm/redhat/brp-ldconfig
+ /usr/lib/rpm/brp-compress
+ /usr/lib/rpm/brp-strip /usr/bin/strip
+ /usr/lib/rpm/brp-strip-comment-note /usr/bin/strip /usr/bin/objdump
+ /usr/lib/rpm/redhat/brp-strip-lto /usr/bin/strip
+ /usr/lib/rpm/check-rpaths
+ /usr/lib/rpm/redhat/brp-mangle-shebangs
+ /usr/lib/rpm/brp-remove-la-files
+ /usr/lib/rpm/redhat/brp-python-rpm-in-distinfo
+ env /usr/lib/rpm/redhat/brp-python-bytecompile '' 1 0 -j2
Bytecompiling .py files below /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14 using python3.14
+ /usr/lib/rpm/redhat/brp-python-hardlink
+ /usr/bin/add-determinism --brp -j2 /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/utils.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/utils_test.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/utils_test.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/utils.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/typing.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/threaded.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/task_spec.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/system.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/tokenize.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/sizeof.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/order.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/rewrite.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/ml.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/local.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/order.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/optimization.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/local.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/optimization.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/layers.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/multiprocessing.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/hashing.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/graph_manipulation.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/highlevelgraph.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/graph_manipulation.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/highlevelgraph.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/distributed.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/datasets.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/dot.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/delayed.cpython-314.opt-1.pyc: replacing with normalized version
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/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/cli.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/callbacks.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/config.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/cache.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/cache.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/blockwise.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/blockwise.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/base.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/backends.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/_version.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/base.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/_task_spec.cpython-314.pyc: replacing with normalized version
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/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/__pycache__/_compatibility.cpython-314.pyc: replacing with normalized version
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/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_percentiles.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_rechunk.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_overlap.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_rechunk.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_numpy_compat.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_numpy_compat.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_masked.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_optimization.cpython-314.opt-1.pyc: replacing with normalized version
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/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_overlap.cpython-314.pyc: replacing with normalized version
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/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_linalg.cpython-314.opt-1.pyc: replacing with normalized version
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/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_image.cpython-314.opt-1.pyc: replacing with normalized version
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/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_gufunc.cpython-314.opt-1.pyc: replacing with normalized version
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/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_fft.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_fft.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_dispatch.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_dispatch.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_sparse.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_sparse.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_slicing.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_routines.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_routines.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_reductions.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_random.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_random.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_percentile.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_percentile.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_array_core.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_overlap.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_overlap.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_linalg.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_linalg.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_gufunc.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_creation.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_creation.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_core.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_cupy_core.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_creation.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_creation.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_chunk.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_chunk.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_atop.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_atop.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_array_utils.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_array_utils.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_array_function.cpython-314.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_array_function.cpython-314.pyc: replacing with normalized version
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/test_api.cpython-314.opt-1.pyc: replacing with normalized version
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/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/tests/__pycache__/__init__.cpython-314.opt-1.pyc: rewriting with normalized contents
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/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/_array_expr/__pycache__/_map_blocks.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/_array_expr/__pycache__/_shuffle.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/_array_expr/__pycache__/_io.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/_array_expr/__pycache__/_reductions.cpython-314.opt-1.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/_array_expr/__pycache__/_rechunk.cpython-314.opt-1.pyc: replacing with normalized version
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/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/array/_array_expr/tests/__pycache__/test_collection.cpython-314.pyc: replacing with normalized version
Scanned 68 directories and 1099 files,
               processed 567 inodes,
               567 modified (414 replaced + 153 rewritten),
               0 unsupported format, 0 errors
Reading /builddir/build/BUILD/python-dask-2025.4.1-build/SPECPARTS/rpm-debuginfo.specpart
Executing(%check): /bin/sh -e /var/tmp/rpm-tmp.txOTKE
+ umask 022
+ cd /builddir/build/BUILD/python-dask-2025.4.1-build
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules  '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ export RUSTFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd dask-2025.4.1
+ k='not test_development_guidelines_matches_ci'
+ pytest_args=(-m 'not network' -n "auto" -k "${k-}" --timeout_method=signal --import-mode=importlib)
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer  '
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes  '
+ PATH=/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/sbin
+ PYTHONPATH=/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib64/python3.14/site-packages:/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages
+ PYTHONDONTWRITEBYTECODE=1
+ PYTEST_ADDOPTS=' --ignore=/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.pyproject-builddir'
+ PYTEST_XDIST_AUTO_NUM_WORKERS=2
+ /usr/bin/pytest -m 'not network' -n auto -k 'not test_development_guidelines_matches_ci' --timeout_method=signal --import-mode=importlib
============================= test session starts ==============================
platform linux -- Python 3.14.0rc1, pytest-8.3.5, pluggy-1.6.0 -- /usr/bin/python3
cachedir: .pytest_cache
hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase(PosixPath('/builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/.hypothesis/examples'))
rootdir: /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1
configfile: pyproject.toml
plugins: timeout-2.4.0, rerunfailures-15.0, mock-3.14.1, xdist-3.7.0, hypothesis-6.123.0
timeout: 300.0s
timeout method: signal
timeout func_only: False
created: 2/2 workers
2 workers [17583 items]

scheduling tests via LoadScheduling

dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__add__] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__add__] 
dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__sub__] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths3-bool] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__sub__] 
dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__mul__] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__mul__] 
dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__truediv__] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__truediv__] 
dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__floordiv__] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__floordiv__] 
dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__pow__] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__pow__] 
dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__radd__] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__radd__] 
dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__rsub__] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__rsub__] 
dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__rmul__] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__rmul__] 
dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__rtruediv__] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__rtruediv__] 
dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__rfloordiv__] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__rfloordiv__] 
dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__rpow__] 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_arithmetic_ops[__rpow__] 
dask/array/_array_expr/tests/test_collection.py::test_rechunk 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_rechunk 
dask/array/_array_expr/tests/test_collection.py::test_blockwise 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_blockwise 
dask/array/_array_expr/tests/test_collection.py::test_reductions[min] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions[min] 
dask/array/_array_expr/tests/test_collection.py::test_reductions[max] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions[max] 
dask/array/_array_expr/tests/test_collection.py::test_reductions[sum] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions[sum] 
dask/array/_array_expr/tests/test_collection.py::test_reductions[prod] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions[prod] 
dask/array/_array_expr/tests/test_collection.py::test_reductions[mean] 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths3-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths4-uint8] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions[mean] 
dask/array/_array_expr/tests/test_collection.py::test_reductions[any] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions[any] 
dask/array/_array_expr/tests/test_collection.py::test_reductions[all] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions[all] 
dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[sum] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[sum] 
dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[mean] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[mean] 
dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[any] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[any] 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths4-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths4-int16] 
dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[all] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[all] 
dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[max] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[max] 
dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[min] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[min] 
dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[nanmin] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[nanmin] 
dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[nanmax] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[nanmax] 
dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[nanmean] 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths4-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths4-float32] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[nanmean] 
dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[nansum] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[nansum] 
dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[nanprod] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_reductions_toplevel[nanprod] 
dask/array/_array_expr/tests/test_collection.py::test_from_array 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_from_array 
dask/array/_array_expr/tests/test_collection.py::test_is_dask_collection_doesnt_materialize 
[gw0] [  0%] SKIPPED dask/array/_array_expr/tests/test_collection.py::test_is_dask_collection_doesnt_materialize 
dask/array/_array_expr/tests/test_collection.py::test_astype 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths4-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths4-bool] 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_astype 
dask/array/_array_expr/tests/test_collection.py::test_stack_promote_type 
[gw0] [  0%] PASSED dask/array/_array_expr/tests/test_collection.py::test_stack_promote_type 
dask/array/tests/test_api.py::test_api 
[gw0] [  0%] PASSED dask/array/tests/test_api.py::test_api 
dask/array/tests/test_array_core.py::test_graph_from_arraylike[True] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_graph_from_arraylike[True] 
dask/array/tests/test_array_core.py::test_graph_from_arraylike[False] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_graph_from_arraylike[False] 
dask/array/tests/test_array_core.py::test_top 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_top 
dask/array/tests/test_array_core.py::test_top_supports_broadcasting_rules 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_top_supports_broadcasting_rules 
dask/array/tests/test_array_core.py::test_top_literals 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_top_literals 
dask/array/tests/test_array_core.py::test_blockwise_literals 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths4-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-2-uint8] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_blockwise_literals 
dask/array/tests/test_array_core.py::test_blockwise_1_in_shape_I 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_blockwise_1_in_shape_I 
dask/array/tests/test_array_core.py::test_blockwise_1_in_shape_II 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_blockwise_1_in_shape_II 
dask/array/tests/test_array_core.py::test_blockwise_1_in_shape_III 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-2-int16] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_blockwise_1_in_shape_III 
dask/array/tests/test_array_core.py::test_concatenate3_on_scalars 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_concatenate3_on_scalars 
dask/array/tests/test_array_core.py::test_chunked_dot_product 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_chunked_dot_product 
dask/array/tests/test_array_core.py::test_chunked_transpose_plus_one 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_chunked_transpose_plus_one 
dask/array/tests/test_array_core.py::test_broadcast_dimensions_works_with_singleton_dimensions 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_dimensions_works_with_singleton_dimensions 
dask/array/tests/test_array_core.py::test_broadcast_dimensions 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_dimensions 
dask/array/tests/test_array_core.py::test_Array 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_Array 
dask/array/tests/test_array_core.py::test_uneven_chunks 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_uneven_chunks 
dask/array/tests/test_array_core.py::test_numblocks_suppoorts_singleton_block_dims 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numblocks_suppoorts_singleton_block_dims 
dask/array/tests/test_array_core.py::test_keys 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_keys 
dask/array/tests/test_array_core.py::test_Array_computation 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_Array_computation 
dask/array/tests/test_array_core.py::test_numpy_asarray_dtype[asarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_dtype[asarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_dtype[asanyarray] 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-2-float32] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_dtype[asanyarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_dtype[array] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_dtype[array] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[5-array] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[5-array] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[5-asarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[5-asarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[5-asanyarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[5-asanyarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[10-array] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[10-array] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[10-asarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[10-asarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[10-asanyarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_true[10-asanyarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[5-array] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[5-array] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[5-asarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[5-asarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[5-asanyarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[5-asanyarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[10-array] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[10-array] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[10-asarray] 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-2-float32] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[10-asarray] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-2-bool] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[10-asanyarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_false[10-asanyarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[5-array] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[5-array] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[5-asarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[5-asarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[5-asanyarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[5-asanyarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[10-array] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[10-array] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[10-asarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[10-asarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[10-asanyarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_none[10-asanyarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[5-asarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[5-asarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[5-asanyarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[5-asanyarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[5-array] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[5-array] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[10-asarray] 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths1-uint8] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[10-asarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[10-asanyarray] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[10-asanyarray] 
dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[10-array] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_numpy_asarray_copy_default[10-array] 
dask/array/tests/test_array_core.py::test_array_interface_deprecated_kwargs 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_array_interface_deprecated_kwargs 
dask/array/tests/test_array_core.py::test_compute_copy[5] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_compute_copy[5] 
dask/array/tests/test_array_core.py::test_compute_copy[10] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_compute_copy[10] 
dask/array/tests/test_array_core.py::test_Array_numpy_gufunc_call__array_ufunc__01 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_Array_numpy_gufunc_call__array_ufunc__01 
dask/array/tests/test_array_core.py::test_Array_numpy_gufunc_call__array_ufunc__02 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths1-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths1-int16] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_Array_numpy_gufunc_call__array_ufunc__02 
dask/array/tests/test_array_core.py::test_stack 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_stack 
dask/array/tests/test_array_core.py::test_stack_zero_size 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_stack_zero_size 
dask/array/tests/test_array_core.py::test_short_stack 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_short_stack 
dask/array/tests/test_array_core.py::test_stack_scalars 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_stack_scalars 
dask/array/tests/test_array_core.py::test_stack_promote_type 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_stack_promote_type 
dask/array/tests/test_array_core.py::test_stack_rechunk 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_stack_rechunk 
dask/array/tests/test_array_core.py::test_stack_unknown_chunksizes 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths1-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths1-float32] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_stack_unknown_chunksizes 
dask/array/tests/test_array_core.py::test_concatenate 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_concatenate 
dask/array/tests/test_array_core.py::test_concatenate_types[dtypes0] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_concatenate_types[dtypes0] 
dask/array/tests/test_array_core.py::test_concatenate_types[dtypes1] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_concatenate_types[dtypes1] 
dask/array/tests/test_array_core.py::test_concatenate_unknown_axes 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_concatenate_unknown_axes 
dask/array/tests/test_array_core.py::test_concatenate_flatten 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths1-float32] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_concatenate_flatten 
dask/array/tests/test_array_core.py::test_concatenate_rechunk 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths1-bool] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_concatenate_rechunk 
dask/array/tests/test_array_core.py::test_concatenate_fixlen_strings 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_concatenate_fixlen_strings 
dask/array/tests/test_array_core.py::test_concatenate_zero_size 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths1-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths2-uint8] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_concatenate_zero_size 
dask/array/tests/test_array_core.py::test_block_simple_row_wise 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_simple_row_wise 
dask/array/tests/test_array_core.py::test_block_simple_column_wise 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_simple_column_wise 
dask/array/tests/test_array_core.py::test_block_with_1d_arrays_row_wise 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_with_1d_arrays_row_wise 
dask/array/tests/test_array_core.py::test_block_with_1d_arrays_multiple_rows 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_with_1d_arrays_multiple_rows 
dask/array/tests/test_array_core.py::test_block_with_1d_arrays_column_wise 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_with_1d_arrays_column_wise 
dask/array/tests/test_array_core.py::test_block_mixed_1d_and_2d 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_mixed_1d_and_2d 
dask/array/tests/test_array_core.py::test_block_complicated 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths2-int16] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_complicated 
dask/array/tests/test_array_core.py::test_block_nested 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_nested 
dask/array/tests/test_array_core.py::test_block_3d 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_3d 
dask/array/tests/test_array_core.py::test_block_with_mismatched_shape 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_with_mismatched_shape 
dask/array/tests/test_array_core.py::test_block_no_lists 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_no_lists 
dask/array/tests/test_array_core.py::test_block_invalid_nesting 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_invalid_nesting 
dask/array/tests/test_array_core.py::test_block_empty_lists 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_empty_lists 
dask/array/tests/test_array_core.py::test_block_tuple 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_block_tuple 
dask/array/tests/test_array_core.py::test_broadcast_shapes 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_shapes 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths2-int16] 
dask/array/tests/test_array_core.py::test_elemwise_on_scalars 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths2-float32] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_elemwise_on_scalars 
dask/array/tests/test_array_core.py::test_elemwise_with_ndarrays 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_elemwise_with_ndarrays 
dask/array/tests/test_array_core.py::test_elemwise_differently_chunked 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_elemwise_differently_chunked 
dask/array/tests/test_array_core.py::test_elemwise_dtype 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths2-bool] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_elemwise_dtype 
dask/array/tests/test_array_core.py::test_operators 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths2-bool] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_operators 
dask/array/tests/test_array_core.py::test_binary_operator_delegation 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths3-uint8] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_binary_operator_delegation 
dask/array/tests/test_array_core.py::test_operator_dtype_promotion 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_operator_dtype_promotion 
dask/array/tests/test_array_core.py::test_field_access 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_field_access 
dask/array/tests/test_array_core.py::test_field_access_with_shape 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths3-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths3-int16] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_field_access_with_shape 
dask/array/tests/test_array_core.py::test_matmul 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths3-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths3-float32] 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths3-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths3-bool] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_matmul 
dask/array/tests/test_array_core.py::test_matmul_array_ufunc 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_matmul_array_ufunc 
dask/array/tests/test_array_core.py::test_T 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_T 
dask/array/tests/test_array_core.py::test_broadcast_to 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_to 
dask/array/tests/test_array_core.py::test_broadcast_to_array 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths3-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths4-uint8] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_to_array 
dask/array/tests/test_array_core.py::test_broadcast_to_scalar 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_to_scalar 
dask/array/tests/test_array_core.py::test_broadcast_to_chunks 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths4-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths4-int16] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_to_chunks 
dask/array/tests/test_array_core.py::test_broadcast_arrays 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_arrays 
dask/array/tests/test_array_core.py::test_broadcast_arrays_uneven_chunks 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths4-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths4-float32] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_arrays_uneven_chunks 
dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape0-v_shape0] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape0-v_shape0] 
dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape1-v_shape1] 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths4-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths4-bool] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape1-v_shape1] 
dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape2-v_shape2] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape2-v_shape2] 
dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape3-v_shape3] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape3-v_shape3] 
dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape4-v_shape4] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape4-v_shape4] 
dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape5-v_shape5] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape5-v_shape5] 
dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape6-v_shape6] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_broadcast_operator[u_shape6-v_shape6] 
dask/array/tests/test_array_core.py::test_reshape[original_shape0-new_shape0-chunks0] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape0-new_shape0-chunks0] 
dask/array/tests/test_array_core.py::test_reshape[original_shape1-new_shape1-5] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape1-new_shape1-5] 
dask/array/tests/test_array_core.py::test_reshape[original_shape2-new_shape2-5] 
[gw1] [  0%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[minimum-pad_widths4-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-2-uint8] 
[gw1] [  0%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-2-int16] 
[gw1] [  0%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-2-int16] 
[gw0] [  0%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape2-new_shape2-5] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-2-float32] 
[gw1] [  0%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-2-bool] 
[gw1] [  0%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths1-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths1-uint8] 
dask/array/tests/test_array_core.py::test_reshape[original_shape3-new_shape3-12] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths1-int16] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape3-new_shape3-12] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths1-int16] 
dask/array/tests/test_array_core.py::test_reshape[original_shape4-new_shape4-12] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths1-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths1-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths1-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths1-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths2-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths2-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths2-float32] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape4-new_shape4-12] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths2-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths2-bool] 
dask/array/tests/test_array_core.py::test_reshape[original_shape5-new_shape5-chunks5] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths3-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths3-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths3-int16] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape5-new_shape5-chunks5] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths3-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths3-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths3-float32] 
dask/array/tests/test_array_core.py::test_reshape[original_shape6-new_shape6-4] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths3-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths3-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths4-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths4-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths4-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths4-int16] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape6-new_shape6-4] 
dask/array/tests/test_array_core.py::test_reshape[original_shape7-new_shape7-4] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths4-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths4-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths4-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[reflect-pad_widths4-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-2-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-2-int16] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape7-new_shape7-4] 
dask/array/tests/test_array_core.py::test_reshape[original_shape8-new_shape8-4] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-2-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-2-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths1-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths1-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths1-int16] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape8-new_shape8-4] 
dask/array/tests/test_array_core.py::test_reshape[original_shape9-new_shape9-2] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths1-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths1-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths1-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths1-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths1-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths2-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths2-int16] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape9-new_shape9-2] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths2-int16] 
dask/array/tests/test_array_core.py::test_reshape[original_shape10-new_shape10-2] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths2-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths2-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths3-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths3-uint8] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape10-new_shape10-2] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths3-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths3-int16] 
dask/array/tests/test_array_core.py::test_reshape[original_shape11-new_shape11-2] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths3-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths3-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths3-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths3-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths4-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths4-uint8] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape11-new_shape11-2] 
dask/array/tests/test_array_core.py::test_reshape[original_shape12-new_shape12-2] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths4-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths4-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths4-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths4-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths4-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[symmetric-pad_widths4-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-2-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-2-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-2-int16] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape12-new_shape12-2] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-2-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-2-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths1-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths1-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths1-int16] 
dask/array/tests/test_array_core.py::test_reshape[original_shape13-new_shape13-2] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths1-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths1-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths1-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths1-bool] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape13-new_shape13-2] 
dask/array/tests/test_array_core.py::test_reshape[original_shape14-new_shape14-2] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths1-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths2-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths2-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths2-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths2-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths3-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths3-uint8] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape14-new_shape14-2] 
dask/array/tests/test_array_core.py::test_reshape[original_shape15-new_shape15-2] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths3-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths3-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths3-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths3-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths3-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths3-bool] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape15-new_shape15-2] 
dask/array/tests/test_array_core.py::test_reshape[original_shape16-new_shape16-chunks16] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths4-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths4-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths4-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths4-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths4-float32] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape16-new_shape16-chunks16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths4-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths4-bool] 
dask/array/tests/test_array_core.py::test_reshape[original_shape17-new_shape17-3] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[wrap-pad_widths4-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-2-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-2-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-2-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-2-float32] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape17-new_shape17-3] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-2-bool] 
dask/array/tests/test_array_core.py::test_reshape[original_shape18-new_shape18-4] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths1-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths1-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths1-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths1-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths1-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths1-float32] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape18-new_shape18-4] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths1-bool] 
dask/array/tests/test_array_core.py::test_reshape[original_shape19-new_shape19-chunks19] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths1-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths2-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths2-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths2-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths2-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths3-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths3-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths3-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths3-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths3-float32] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape19-new_shape19-chunks19] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths3-float32] 
dask/array/tests/test_array_core.py::test_reshape[original_shape20-new_shape20-1] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths3-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths3-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths4-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths4-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths4-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths4-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths4-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths4-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths4-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[median-pad_widths4-bool] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape20-new_shape20-1] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-2-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-2-int16] 
dask/array/tests/test_array_core.py::test_reshape[original_shape21-new_shape21-1] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-2-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-2-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths1-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths1-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths1-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths1-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths1-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths1-float32] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape21-new_shape21-1] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths1-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths1-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths2-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths2-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths2-float32] 
dask/array/tests/test_array_core.py::test_reshape[original_shape22-new_shape22-24] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths2-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths2-bool] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape22-new_shape22-24] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths3-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths3-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths3-int16] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths3-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths3-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths3-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths3-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths3-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths4-uint8] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths4-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths4-int16] 
dask/array/tests/test_array_core.py::test_reshape[original_shape23-new_shape23-6] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths4-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths4-float32] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths4-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths4-bool] 
[gw1] [  1%] SKIPPED dask/array/tests/test_creation.py::test_pad_3d_data[empty-pad_widths4-bool] 
dask/array/tests/test_creation.py::test_pad_udf[kwargs0] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape23-new_shape23-6] 
dask/array/tests/test_array_core.py::test_reshape[original_shape24-new_shape24-6] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape24-new_shape24-6] 
dask/array/tests/test_array_core.py::test_reshape[original_shape25-new_shape25-6] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape25-new_shape25-6] 
dask/array/tests/test_array_core.py::test_reshape[original_shape26-new_shape26-chunks26] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape26-new_shape26-chunks26] 
dask/array/tests/test_array_core.py::test_reshape[original_shape27-new_shape27-chunks27] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape27-new_shape27-chunks27] 
dask/array/tests/test_array_core.py::test_reshape[original_shape28-new_shape28-chunks28] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape28-new_shape28-chunks28] 
dask/array/tests/test_array_core.py::test_reshape[original_shape29-new_shape29-chunks29] 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_pad_udf[kwargs0] 
dask/array/tests/test_creation.py::test_pad_udf[kwargs1] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape29-new_shape29-chunks29] 
dask/array/tests/test_array_core.py::test_reshape[original_shape30-new_shape30-chunks30] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape30-new_shape30-chunks30] 
dask/array/tests/test_array_core.py::test_reshape[original_shape31-new_shape31-chunks31] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape31-new_shape31-chunks31] 
dask/array/tests/test_array_core.py::test_reshape[original_shape32-new_shape32-chunks32] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape32-new_shape32-chunks32] 
dask/array/tests/test_array_core.py::test_reshape[original_shape33-new_shape33-chunks33] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape33-new_shape33-chunks33] 
dask/array/tests/test_array_core.py::test_reshape[original_shape34-new_shape34-chunks34] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape[original_shape34-new_shape34-chunks34] 
dask/array/tests/test_array_core.py::test_reshape_exceptions 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_pad_udf[kwargs1] 
dask/array/tests/test_creation.py::test_pad_constant_chunksizes 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape_exceptions 
dask/array/tests/test_array_core.py::test_reshape_splat 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape_splat 
dask/array/tests/test_array_core.py::test_reshape_not_implemented_error 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape_not_implemented_error 
dask/array/tests/test_array_core.py::test_reshape_unknown_dimensions 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_reshape_unknown_dimensions 
dask/array/tests/test_array_core.py::test_full 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_full 
dask/array/tests/test_array_core.py::test_map_blocks 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_pad_constant_chunksizes 
dask/array/tests/test_creation.py::test_auto_chunks 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_auto_chunks 
dask/array/tests/test_creation.py::test_string_auto_chunk 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_string_auto_chunk 
dask/array/tests/test_creation.py::test_diagonal_zero_chunks 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks 
dask/array/tests/test_array_core.py::test_map_blocks2 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_diagonal_zero_chunks 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[u4-shape_chunks0-zeros_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks2 
dask/array/tests/test_array_core.py::test_map_blocks_block_info 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[u4-shape_chunks0-zeros_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[u4-shape_chunks0-ones_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_block_info 
dask/array/tests/test_array_core.py::test_map_blocks_block_info_with_new_axis 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_block_info_with_new_axis 
dask/array/tests/test_array_core.py::test_map_blocks_block_info_with_drop_axis 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[u4-shape_chunks0-ones_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[u4-shape_chunks1-zeros_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_block_info_with_drop_axis 
dask/array/tests/test_array_core.py::test_map_blocks_block_info_with_broadcast 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[u4-shape_chunks1-zeros_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[u4-shape_chunks1-ones_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_block_info_with_broadcast 
dask/array/tests/test_array_core.py::test_map_blocks_with_constants 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[u4-shape_chunks1-ones_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[float32-shape_chunks0-zeros_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_with_constants 
dask/array/tests/test_array_core.py::test_map_blocks_with_kwargs 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_with_kwargs 
dask/array/tests/test_array_core.py::test_map_blocks_infer_chunks_broadcast 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_infer_chunks_broadcast 
dask/array/tests/test_array_core.py::test_map_blocks_with_chunks 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[float32-shape_chunks0-zeros_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[float32-shape_chunks0-ones_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_with_chunks 
dask/array/tests/test_array_core.py::test_map_blocks_dtype_inference 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[float32-shape_chunks0-ones_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[float32-shape_chunks1-zeros_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_dtype_inference 
dask/array/tests/test_array_core.py::test_map_blocks_infer_newaxis 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[float32-shape_chunks1-zeros_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[float32-shape_chunks1-ones_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_infer_newaxis 
dask/array/tests/test_array_core.py::test_map_blocks_no_array_args 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_no_array_args 
dask/array/tests/test_array_core.py::test_map_blocks_unique_name_enforce_dim 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[float32-shape_chunks1-ones_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[None-shape_chunks0-zeros_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_unique_name_enforce_dim 
dask/array/tests/test_array_core.py::test_map_blocks_unique_name_chunks_dtype 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_unique_name_chunks_dtype 
dask/array/tests/test_array_core.py::test_map_blocks_unique_name_drop_axis 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[None-shape_chunks0-zeros_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[None-shape_chunks0-ones_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_unique_name_drop_axis 
dask/array/tests/test_array_core.py::test_map_blocks_unique_name_new_axis 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_unique_name_new_axis 
dask/array/tests/test_array_core.py::test_map_blocks_optimize_blockwise[<lambda>0] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_optimize_blockwise[<lambda>0] 
dask/array/tests/test_array_core.py::test_map_blocks_optimize_blockwise[<lambda>1] 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[None-shape_chunks0-ones_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[None-shape_chunks1-zeros_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_optimize_blockwise[<lambda>1] 
dask/array/tests/test_array_core.py::test_repr 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_repr 
dask/array/tests/test_array_core.py::test_repr_meta 
[gw0] [  1%] SKIPPED dask/array/tests/test_array_core.py::test_repr_meta 
dask/array/tests/test_array_core.py::test_repr_html_array_highlevelgraph 
[gw0] [  1%] SKIPPED dask/array/tests/test_array_core.py::test_repr_html_array_highlevelgraph 
dask/array/tests/test_array_core.py::test_slicing_with_ellipsis 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[None-shape_chunks1-zeros_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[None-shape_chunks1-ones_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_slicing_with_ellipsis 
dask/array/tests/test_array_core.py::test_slicing_with_ndarray 
[gw1] [  1%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[None-shape_chunks1-ones_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[int64-shape_chunks0-zeros_like] 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_slicing_with_ndarray 
dask/array/tests/test_array_core.py::test_slicing_flexible_type 
[gw0] [  1%] PASSED dask/array/tests/test_array_core.py::test_slicing_flexible_type 
dask/array/tests/test_array_core.py::test_slicing_with_object_dtype 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_slicing_with_object_dtype 
dask/array/tests/test_array_core.py::test_dtype 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[int64-shape_chunks0-zeros_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[int64-shape_chunks0-ones_like] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_dtype 
dask/array/tests/test_array_core.py::test_blockdims_from_blockshape 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_blockdims_from_blockshape 
dask/array/tests/test_array_core.py::test_coerce 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_coerce 
dask/array/tests/test_array_core.py::test_bool 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_bool 
dask/array/tests/test_array_core.py::test_store_kwargs 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[int64-shape_chunks0-ones_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[int64-shape_chunks1-zeros_like] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[int64-shape_chunks1-zeros_like] 
dask/array/tests/test_creation.py::test_nan_zeros_ones_like[int64-shape_chunks1-ones_like] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_kwargs 
dask/array/tests/test_array_core.py::test_store_delayed_target 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_zeros_ones_like[int64-shape_chunks1-ones_like] 
dask/array/tests/test_creation.py::test_from_array_getitem_fused 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_from_array_getitem_fused 
dask/array/tests/test_creation.py::test_nan_empty_like[u4-shape_chunks0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_empty_like[u4-shape_chunks0] 
dask/array/tests/test_creation.py::test_nan_empty_like[u4-shape_chunks1] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_empty_like[u4-shape_chunks1] 
dask/array/tests/test_creation.py::test_nan_empty_like[float32-shape_chunks0] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_delayed_target 
dask/array/tests/test_array_core.py::test_store 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store 
dask/array/tests/test_array_core.py::test_store_regions 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_empty_like[float32-shape_chunks0] 
dask/array/tests/test_creation.py::test_nan_empty_like[float32-shape_chunks1] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_empty_like[float32-shape_chunks1] 
dask/array/tests/test_creation.py::test_nan_empty_like[None-shape_chunks0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_empty_like[None-shape_chunks0] 
dask/array/tests/test_creation.py::test_nan_empty_like[None-shape_chunks1] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_empty_like[None-shape_chunks1] 
dask/array/tests/test_creation.py::test_nan_empty_like[int64-shape_chunks0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_empty_like[int64-shape_chunks0] 
dask/array/tests/test_creation.py::test_nan_empty_like[int64-shape_chunks1] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_empty_like[int64-shape_chunks1] 
dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks0-0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks0-0] 
dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks0-0.0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks0-0.0] 
dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks0-99] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks0-99] 
dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks0--1] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_regions 
dask/array/tests/test_array_core.py::test_store_compute_false 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_compute_false 
dask/array/tests/test_array_core.py::test_store_nocompute_regions 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_nocompute_regions 
dask/array/tests/test_array_core.py::test_store_locks_failure_lock_released 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_locks_failure_lock_released 
dask/array/tests/test_array_core.py::test_store_locks 
[gw1] [  2%] XFAIL dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks0--1] 
dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks1-0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks1-0] 
dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks1-0.0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks1-0.0] 
dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks1-99] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks1-99] 
dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks1--1] 
[gw1] [  2%] XFAIL dask/array/tests/test_creation.py::test_nan_full_like[u4-shape_chunks1--1] 
dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks0-0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks0-0] 
dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks0-0.0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks0-0.0] 
dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks0-99] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks0-99] 
dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks0--1] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks0--1] 
dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks1-0] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_locks 
dask/array/tests/test_array_core.py::test_store_method_return 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks1-0] 
dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks1-0.0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks1-0.0] 
dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks1-99] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_method_return 
dask/array/tests/test_array_core.py::test_store_multiprocessing_lock 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks1-99] 
dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks1--1] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[float32-shape_chunks1--1] 
dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks0-0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks0-0] 
dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks0-0.0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks0-0.0] 
dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks0-99] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks0-99] 
dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks0--1] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks0--1] 
dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks1-0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks1-0] 
dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks1-0.0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks1-0.0] 
dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks1-99] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks1-99] 
dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks1--1] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[None-shape_chunks1--1] 
dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks0-0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks0-0] 
dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks0-0.0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks0-0.0] 
dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks0-99] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks0-99] 
dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks0--1] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks0--1] 
dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks1-0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks1-0] 
dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks1-0.0] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks1-0.0] 
dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks1-99] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks1-99] 
dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks1--1] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_nan_full_like[int64-shape_chunks1--1] 
dask/array/tests/test_creation.py::test_like_forgets_graph[array] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_like_forgets_graph[array] 
dask/array/tests/test_creation.py::test_like_forgets_graph[asarray] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_like_forgets_graph[asarray] 
dask/array/tests/test_creation.py::test_like_forgets_graph[asanyarray] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_like_forgets_graph[asanyarray] 
dask/array/tests/test_creation.py::test_like_forgets_graph[arange] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_like_forgets_graph[arange] 
dask/array/tests/test_creation.py::test_like_forgets_graph[tri] 
[gw1] [  2%] PASSED dask/array/tests/test_creation.py::test_like_forgets_graph[tri] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-add0] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-add0] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-eq] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-eq] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-gt] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-gt] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-ge] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-ge] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-lt] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-lt] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-le] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-le] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-mod] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-mod] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-mul] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-mul] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-ne] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-ne] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-pow] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-pow] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-sub] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-sub] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-truediv] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-truediv] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-floordiv] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-floordiv] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-add1] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-add1] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-subtract] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast0-arr_downcast0-subtract] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-add0] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-add0] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-eq] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-eq] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-gt] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-gt] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-ge] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-ge] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-lt] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-lt] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-le] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-le] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-mod] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-mod] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-mul] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-mul] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-ne] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-ne] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-pow] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-pow] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-sub] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-sub] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-truediv] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-truediv] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-floordiv] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-floordiv] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-add1] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-add1] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-subtract] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast1-arr_downcast1-subtract] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-add0] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-add0] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-eq] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-eq] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-gt] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-gt] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-ge] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-ge] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-lt] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-lt] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-le] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-le] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-mod] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-mod] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-mul] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-mul] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-ne] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-ne] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-pow] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-pow] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-sub] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-sub] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-truediv] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-truediv] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-floordiv] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-floordiv] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-add1] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-add1] 
dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-subtract] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_binary_operation_type_precedence[arr_upcast2-arr_downcast2-subtract] 
dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[arr0-False] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[arr0-False] 
dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[arr1-False] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[arr1-False] 
dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[arr2-True] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[arr2-True] 
dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[arr3-True] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[arr3-True] 
dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[arr4-True] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[arr4-True] 
dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[None-True] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[None-True] 
dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[0.0-False] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[0.0-False] 
dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[0-False] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[0-False] 
dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[-False] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_array_chunk[-False] 
dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[WrappedArray-False] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[WrappedArray-False] 
dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[Array-False] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[Array-False] 
dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[EncapsulateNDArray-True] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[EncapsulateNDArray-True] 
dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[MaskedArray-True] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[MaskedArray-True] 
dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[ndarray-True] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[ndarray-True] 
dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[float-False] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[float-False] 
dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[int-False] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_is_valid_chunk_type[int-False] 
dask/array/tests/test_dispatch.py::test_direct_deferral_wrapping_override 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_direct_deferral_wrapping_override 
dask/array/tests/test_dispatch.py::test_delegation_unknown_scalar_that_understands_arr_ops[arr0] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_delegation_unknown_scalar_that_understands_arr_ops[arr0] 
dask/array/tests/test_dispatch.py::test_delegation_unknown_scalar_that_understands_arr_ops[arr1] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_delegation_unknown_scalar_that_understands_arr_ops[arr1] 
dask/array/tests/test_dispatch.py::test_delegation_unknown_scalar[arr0] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_delegation_unknown_scalar[arr0] 
dask/array/tests/test_dispatch.py::test_delegation_unknown_scalar[arr1] 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_delegation_unknown_scalar[arr1] 
dask/array/tests/test_dispatch.py::test_delegation_specific_cases 
[gw1] [  2%] PASSED dask/array/tests/test_dispatch.py::test_delegation_specific_cases 
dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[fft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[fft] 
dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[ifft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[ifft] 
dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[rfft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[rfft] 
dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[irfft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[irfft] 
dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[hfft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[hfft] 
dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[ihfft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_cant_fft_chunked_axis[ihfft] 
dask/array/tests/test_fft.py::test_fft[fft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft[fft] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_multiprocessing_lock 
dask/array/tests/test_fft.py::test_fft[ifft] 
dask/array/tests/test_array_core.py::test_store_deterministic_keys[False-False] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft[ifft] 
dask/array/tests/test_fft.py::test_fft[rfft] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_deterministic_keys[False-False] 
dask/array/tests/test_array_core.py::test_store_deterministic_keys[False-True] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft[rfft] 
dask/array/tests/test_fft.py::test_fft[irfft] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_deterministic_keys[False-True] 
dask/array/tests/test_array_core.py::test_store_deterministic_keys[True-False] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft[irfft] 
dask/array/tests/test_fft.py::test_fft[hfft] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_deterministic_keys[True-False] 
dask/array/tests/test_array_core.py::test_store_deterministic_keys[True-True] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft[hfft] 
dask/array/tests/test_fft.py::test_fft[ihfft] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_store_deterministic_keys[True-True] 
dask/array/tests/test_array_core.py::test_to_hdf5 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft[ihfft] 
dask/array/tests/test_fft.py::test_fft2n_shapes[fft2] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft2n_shapes[fft2] 
dask/array/tests/test_fft.py::test_fft2n_shapes[ifft2] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft2n_shapes[ifft2] 
dask/array/tests/test_fft.py::test_fft2n_shapes[fftn] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft2n_shapes[fftn] 
dask/array/tests/test_fft.py::test_fft2n_shapes[ifftn] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft2n_shapes[ifftn] 
dask/array/tests/test_fft.py::test_fft2n_shapes[rfft2] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft2n_shapes[rfft2] 
dask/array/tests/test_fft.py::test_fft2n_shapes[irfft2] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft2n_shapes[irfft2] 
dask/array/tests/test_fft.py::test_fft2n_shapes[rfftn] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_to_hdf5 
dask/array/tests/test_array_core.py::test_to_dask_dataframe 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft2n_shapes[rfftn] 
dask/array/tests/test_fft.py::test_fft2n_shapes[irfftn] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_to_dask_dataframe 
dask/array/tests/test_array_core.py::test_np_array_with_zero_dimensions 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft2n_shapes[irfftn] 
dask/array/tests/test_fft.py::test_fft_n_kwarg[fft] 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_np_array_with_zero_dimensions 
dask/array/tests/test_array_core.py::test_dtype_complex 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_dtype_complex 
dask/array/tests/test_array_core.py::test_astype 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_astype 
dask/array/tests/test_array_core.py::test_astype_gh1151 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_astype_gh1151 
dask/array/tests/test_array_core.py::test_astype_gh9318 
[gw0] [  2%] PASSED dask/array/tests/test_array_core.py::test_astype_gh9318 
dask/array/tests/test_array_core.py::test_astype_gh9316 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_n_kwarg[fft] 
dask/array/tests/test_fft.py::test_fft_n_kwarg[ifft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_n_kwarg[ifft] 
dask/array/tests/test_fft.py::test_fft_n_kwarg[rfft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_n_kwarg[rfft] 
dask/array/tests/test_fft.py::test_fft_n_kwarg[irfft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_n_kwarg[irfft] 
dask/array/tests/test_fft.py::test_fft_n_kwarg[hfft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_n_kwarg[hfft] 
dask/array/tests/test_fft.py::test_fft_n_kwarg[ihfft] 
[gw0] [  2%] XFAIL dask/array/tests/test_array_core.py::test_astype_gh9316 
dask/array/tests/test_array_core.py::test_arithmetic 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_n_kwarg[ihfft] 
dask/array/tests/test_fft.py::test_fft_consistent_names[fft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_consistent_names[fft] 
dask/array/tests/test_fft.py::test_fft_consistent_names[ifft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_consistent_names[ifft] 
dask/array/tests/test_fft.py::test_fft_consistent_names[rfft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_consistent_names[rfft] 
dask/array/tests/test_fft.py::test_fft_consistent_names[irfft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_consistent_names[irfft] 
dask/array/tests/test_fft.py::test_fft_consistent_names[hfft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_consistent_names[hfft] 
dask/array/tests/test_fft.py::test_fft_consistent_names[ihfft] 
[gw1] [  2%] PASSED dask/array/tests/test_fft.py::test_fft_consistent_names[ihfft] 
dask/array/tests/test_fft.py::test_wrap_bad_kind 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_bad_kind 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-fft2] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-fft2] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-ifft2] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-ifft2] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-fftn] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-fftn] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-ifftn] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-ifftn] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-rfft2] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-rfft2] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-irfft2] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-irfft2] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-rfftn] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-rfftn] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-irfftn] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_arithmetic 
dask/array/tests/test_array_core.py::test_elemwise_consistent_names 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_elemwise_consistent_names 
dask/array/tests/test_array_core.py::test_optimize 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_optimize 
dask/array/tests/test_array_core.py::test_slicing_with_non_ndarrays 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_slicing_with_non_ndarrays 
dask/array/tests/test_array_core.py::test_getter 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_getter 
dask/array/tests/test_array_core.py::test_size 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float32-irfftn] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-fft2] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_size 
dask/array/tests/test_array_core.py::test_nbytes 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_nbytes 
dask/array/tests/test_array_core.py::test_itemsize 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_itemsize 
dask/array/tests/test_array_core.py::test_Array_normalizes_dtype 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_Array_normalizes_dtype 
dask/array/tests/test_array_core.py::test_from_array_with_lock[True] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_with_lock[True] 
dask/array/tests/test_array_core.py::test_from_array_with_lock[False] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_with_lock[False] 
dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[True-x0-chunks0] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[True-x0-chunks0] 
dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[True-x1--1] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[True-x1--1] 
dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[True-x2-1] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[True-x2-1] 
dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[True-x3-1] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[True-x3-1] 
dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[False-x0-chunks0] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[False-x0-chunks0] 
dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[False-x1--1] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[False-x1--1] 
dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[False-x2-1] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[False-x2-1] 
dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[False-x3-1] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_tasks_always_call_getter[False-x3-1] 
dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x0] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x0] 
dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x1] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-fft2] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-ifft2] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x1] 
dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x2] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x2] 
dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x3] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x3] 
dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x4] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x4] 
dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x5] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_ndarray_onechunk[x5] 
dask/array/tests/test_array_core.py::test_from_array_ndarray_getitem 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_ndarray_getitem 
dask/array/tests/test_array_core.py::test_from_array_list[x0] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_list[x0] 
dask/array/tests/test_array_core.py::test_from_array_list[x1] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_list[x1] 
dask/array/tests/test_array_core.py::test_from_array_list[x2] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_list[x2] 
dask/array/tests/test_array_core.py::test_from_array_scalar[bool0] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[bool0] 
dask/array/tests/test_array_core.py::test_from_array_scalar[bytes] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[bytes] 
dask/array/tests/test_array_core.py::test_from_array_scalar[complex] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[complex] 
dask/array/tests/test_array_core.py::test_from_array_scalar[float] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[float] 
dask/array/tests/test_array_core.py::test_from_array_scalar[int] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[int] 
dask/array/tests/test_array_core.py::test_from_array_scalar[bool1] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-ifft2] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-fftn] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[bool1] 
dask/array/tests/test_array_core.py::test_from_array_scalar[bytes_] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[bytes_] 
dask/array/tests/test_array_core.py::test_from_array_scalar[clongdouble] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[clongdouble] 
dask/array/tests/test_array_core.py::test_from_array_scalar[complex128] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[complex128] 
dask/array/tests/test_array_core.py::test_from_array_scalar[complex64] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[complex64] 
dask/array/tests/test_array_core.py::test_from_array_scalar[datetime64] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[datetime64] 
dask/array/tests/test_array_core.py::test_from_array_scalar[float16] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[float16] 
dask/array/tests/test_array_core.py::test_from_array_scalar[float32] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[float32] 
dask/array/tests/test_array_core.py::test_from_array_scalar[float64] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[float64] 
dask/array/tests/test_array_core.py::test_from_array_scalar[int16] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[int16] 
dask/array/tests/test_array_core.py::test_from_array_scalar[int32] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[int32] 
dask/array/tests/test_array_core.py::test_from_array_scalar[int64] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[int64] 
dask/array/tests/test_array_core.py::test_from_array_scalar[int8] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[int8] 
dask/array/tests/test_array_core.py::test_from_array_scalar[longdouble] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[longdouble] 
dask/array/tests/test_array_core.py::test_from_array_scalar[longlong] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[longlong] 
dask/array/tests/test_array_core.py::test_from_array_scalar[object_] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[object_] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-fftn] 
dask/array/tests/test_array_core.py::test_from_array_scalar[str_] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-ifftn] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[str_] 
dask/array/tests/test_array_core.py::test_from_array_scalar[timedelta64] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[timedelta64] 
dask/array/tests/test_array_core.py::test_from_array_scalar[uint16] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[uint16] 
dask/array/tests/test_array_core.py::test_from_array_scalar[uint32] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[uint32] 
dask/array/tests/test_array_core.py::test_from_array_scalar[uint64] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[uint64] 
dask/array/tests/test_array_core.py::test_from_array_scalar[uint8] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[uint8] 
dask/array/tests/test_array_core.py::test_from_array_scalar[ulonglong] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[ulonglong] 
dask/array/tests/test_array_core.py::test_from_array_scalar[void] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[void] 
dask/array/tests/test_array_core.py::test_from_array_scalar[str] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_scalar[str] 
dask/array/tests/test_array_core.py::test_from_array_no_asarray[True-True-ndarray] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_no_asarray[True-True-ndarray] 
dask/array/tests/test_array_core.py::test_from_array_no_asarray[True-False-matrix] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_no_asarray[True-False-matrix] 
dask/array/tests/test_array_core.py::test_from_array_no_asarray[False-True-ndarray] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_no_asarray[False-True-ndarray] 
dask/array/tests/test_array_core.py::test_from_array_no_asarray[False-False-matrix] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_no_asarray[False-False-matrix] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-ifftn] 
dask/array/tests/test_array_core.py::test_from_array_getitem[True-True] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-rfft2] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_getitem[True-True] 
dask/array/tests/test_array_core.py::test_from_array_getitem[True-False] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_getitem[True-False] 
dask/array/tests/test_array_core.py::test_from_array_getitem[False-True] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_getitem[False-True] 
dask/array/tests/test_array_core.py::test_from_array_getitem[False-False] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_getitem[False-False] 
dask/array/tests/test_array_core.py::test_from_array_minus_one 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_minus_one 
dask/array/tests/test_array_core.py::test_array_copy_noop[-1] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_array_copy_noop[-1] 
dask/array/tests/test_array_core.py::test_array_copy_noop[2] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_array_copy_noop[2] 
dask/array/tests/test_array_core.py::test_from_array_dask_array 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_dask_array 
dask/array/tests/test_array_core.py::test_from_array_dask_collection_warns 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_dask_collection_warns 
dask/array/tests/test_array_core.py::test_from_array_inline 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_inline 
dask/array/tests/test_array_core.py::test_asarray[asarray] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asarray[asarray] 
dask/array/tests/test_array_core.py::test_asarray[asanyarray] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asarray[asanyarray] 
dask/array/tests/test_array_core.py::test_asarray_array_dtype[asarray] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-rfft2] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-irfft2] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asarray_array_dtype[asarray] 
dask/array/tests/test_array_core.py::test_asarray_array_dtype[asanyarray] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asarray_array_dtype[asanyarray] 
dask/array/tests/test_array_core.py::test_asarray_dask_dataframe[asarray] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asarray_dask_dataframe[asarray] 
dask/array/tests/test_array_core.py::test_asarray_dask_dataframe[asanyarray] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asarray_dask_dataframe[asanyarray] 
dask/array/tests/test_array_core.py::test_asarray_h5py[True-asarray] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asarray_h5py[True-asarray] 
dask/array/tests/test_array_core.py::test_asarray_h5py[True-asanyarray] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asarray_h5py[True-asanyarray] 
dask/array/tests/test_array_core.py::test_asarray_h5py[False-asarray] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asarray_h5py[False-asarray] 
dask/array/tests/test_array_core.py::test_asarray_h5py[False-asanyarray] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asarray_h5py[False-asanyarray] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-irfft2] 
dask/array/tests/test_array_core.py::test_asarray_chunks 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-rfftn] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asarray_chunks 
dask/array/tests/test_array_core.py::test_asanyarray 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asanyarray 
dask/array/tests/test_array_core.py::test_asanyarray_dataframe 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asanyarray_dataframe 
dask/array/tests/test_array_core.py::test_asanyarray_datetime64 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_asanyarray_datetime64 
dask/array/tests/test_array_core.py::test_from_func 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_func 
dask/array/tests/test_array_core.py::test_concatenate3_2 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_concatenate3_2 
dask/array/tests/test_array_core.py::test_map_blocks3 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_map_blocks3 
dask/array/tests/test_array_core.py::test_from_array_with_missing_chunks 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_from_array_with_missing_chunks 
dask/array/tests/test_array_core.py::test_normalize_chunks[normalize_chunks] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks[normalize_chunks] 
dask/array/tests/test_array_core.py::test_normalize_chunks[normalize_chunks_cached] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks[normalize_chunks_cached] 
dask/array/tests/test_array_core.py::test_single_element_tuple 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_single_element_tuple 
dask/array/tests/test_array_core.py::test_align_chunks_to_previous_chunks 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_align_chunks_to_previous_chunks 
dask/array/tests/test_array_core.py::test_raise_on_no_chunks 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_raise_on_no_chunks 
dask/array/tests/test_array_core.py::test_chunks_is_immutable 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_chunks_is_immutable 
dask/array/tests/test_array_core.py::test_raise_on_bad_kwargs 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-rfftn] 
dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-irfftn] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_raise_on_bad_kwargs 
dask/array/tests/test_array_core.py::test_long_slice 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_nd_ffts_axes[float64-irfftn] 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-fft-numpy.fft] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_long_slice 
dask/array/tests/test_array_core.py::test_h5py_newaxis 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_h5py_newaxis 
dask/array/tests/test_array_core.py::test_ellipsis_slicing 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-fft-numpy.fft] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_ellipsis_slicing 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-fft-scipy.fft] 
dask/array/tests/test_array_core.py::test_point_slicing 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_point_slicing 
dask/array/tests/test_array_core.py::test_point_slicing_with_full_slice 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_point_slicing_with_full_slice 
dask/array/tests/test_array_core.py::test_slice_with_floats 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_slice_with_floats 
dask/array/tests/test_array_core.py::test_slice_with_integer_types[int32] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-fft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-ifft-numpy.fft] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_slice_with_integer_types[int32] 
dask/array/tests/test_array_core.py::test_slice_with_integer_types[int64] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_slice_with_integer_types[int64] 
dask/array/tests/test_array_core.py::test_slice_with_integer_types[uint32] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_slice_with_integer_types[uint32] 
dask/array/tests/test_array_core.py::test_slice_with_integer_types[uint64] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_slice_with_integer_types[uint64] 
dask/array/tests/test_array_core.py::test_index_with_integer_types[int] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_index_with_integer_types[int] 
dask/array/tests/test_array_core.py::test_index_with_integer_types[int32] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_index_with_integer_types[int32] 
dask/array/tests/test_array_core.py::test_index_with_integer_types[int64] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_index_with_integer_types[int64] 
dask/array/tests/test_array_core.py::test_index_with_integer_types[uint32] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_index_with_integer_types[uint32] 
dask/array/tests/test_array_core.py::test_index_with_integer_types[uint64] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_index_with_integer_types[uint64] 
dask/array/tests/test_array_core.py::test_vindex_basic 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_vindex_basic 
dask/array/tests/test_array_core.py::test_vindex_nd 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-ifft-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-ifft-scipy.fft] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_vindex_nd 
dask/array/tests/test_array_core.py::test_vindex_preserve_chunksize[0] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_vindex_preserve_chunksize[0] 
dask/array/tests/test_array_core.py::test_vindex_preserve_chunksize[1] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-ifft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-rfft-numpy.fft] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_vindex_preserve_chunksize[1] 
dask/array/tests/test_array_core.py::test_vindex_negative 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_vindex_negative 
dask/array/tests/test_array_core.py::test_vindex_errors 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_vindex_errors 
dask/array/tests/test_array_core.py::test_vindex_merge 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-rfft-numpy.fft] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_vindex_merge 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-rfft-scipy.fft] 
dask/array/tests/test_array_core.py::test_vindex_identity 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_vindex_identity 
dask/array/tests/test_array_core.py::test_empty_array 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_empty_array 
dask/array/tests/test_array_core.py::test_memmap 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_memmap 
dask/array/tests/test_array_core.py::test_to_npy_stack 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_to_npy_stack 
dask/array/tests/test_array_core.py::test_view 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_view 
dask/array/tests/test_array_core.py::test_view_fortran 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-rfft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-irfft-numpy.fft] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_view_fortran 
dask/array/tests/test_array_core.py::test_h5py_tokenize 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_h5py_tokenize 
dask/array/tests/test_array_core.py::test_map_blocks_with_changed_dimension 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_with_changed_dimension 
dask/array/tests/test_array_core.py::test_map_blocks_with_negative_drop_axis 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_with_negative_drop_axis 
dask/array/tests/test_array_core.py::test_map_blocks_with_invalid_drop_axis 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_with_invalid_drop_axis 
dask/array/tests/test_array_core.py::test_map_blocks_with_changed_dimension_and_broadcast_chunks 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-irfft-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-irfft-scipy.fft] 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_with_changed_dimension_and_broadcast_chunks 
dask/array/tests/test_array_core.py::test_broadcast_chunks 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_broadcast_chunks 
dask/array/tests/test_array_core.py::test_chunks_error 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_chunks_error 
dask/array/tests/test_array_core.py::test_array_compute_forward_kwargs 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_array_compute_forward_kwargs 
dask/array/tests/test_array_core.py::test_dont_fuse_outputs 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_dont_fuse_outputs 
dask/array/tests/test_array_core.py::test_dont_dealias_outputs 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_dont_dealias_outputs 
dask/array/tests/test_array_core.py::test_timedelta_op 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_timedelta_op 
dask/array/tests/test_array_core.py::test_to_delayed 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_to_delayed 
dask/array/tests/test_array_core.py::test_to_delayed_optimize_graph 
[gw0] [  3%] PASSED dask/array/tests/test_array_core.py::test_to_delayed_optimize_graph 
dask/array/tests/test_array_core.py::test_cumulative 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-irfft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-hfft-numpy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-hfft-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-hfft-scipy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-hfft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-ihfft-numpy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-ihfft-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float32-ihfft-scipy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float32-ihfft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-fft-numpy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-fft-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-fft-scipy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-fft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-ifft-numpy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-ifft-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-ifft-scipy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-ifft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-rfft-numpy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-rfft-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-rfft-scipy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-rfft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-irfft-numpy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-irfft-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-irfft-scipy.fft] 
[gw1] [  3%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-irfft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-hfft-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-hfft-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-hfft-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-hfft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-ihfft-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-ihfft-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_ffts[float64-ihfft-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_ffts[float64-ihfft-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-fft2-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-fft2-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-fft2-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-fft2-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-ifft2-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-ifft2-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-ifft2-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-ifft2-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-fftn-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-fftn-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-fftn-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-fftn-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-ifftn-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-ifftn-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-ifftn-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-ifftn-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-rfft2-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-rfft2-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-rfft2-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-rfft2-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-irfft2-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-irfft2-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-irfft2-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-irfft2-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-rfftn-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-rfftn-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-rfftn-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-rfftn-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-irfftn-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-irfftn-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float32-irfftn-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float32-irfftn-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-fft2-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-fft2-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-fft2-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-fft2-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-ifft2-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-ifft2-numpy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_cumulative 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-ifft2-scipy.fft] 
dask/array/tests/test_array_core.py::test_from_delayed 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_from_delayed 
dask/array/tests/test_array_core.py::test_from_delayed_meta 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_from_delayed_meta 
dask/array/tests/test_array_core.py::test_from_delayed_future 
[gw0] [  4%] SKIPPED dask/array/tests/test_array_core.py::test_from_delayed_future 
dask/array/tests/test_array_core.py::test_A_property 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_A_property 
dask/array/tests/test_array_core.py::test_copy_mutate 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_copy_mutate 
dask/array/tests/test_array_core.py::test_npartitions 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_npartitions 
dask/array/tests/test_array_core.py::test_elemwise_name 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-ifft2-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-fftn-numpy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_elemwise_name 
dask/array/tests/test_array_core.py::test_map_blocks_name 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_name 
dask/array/tests/test_array_core.py::test_map_blocks_token_deprecated 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_token_deprecated 
dask/array/tests/test_array_core.py::test_from_array_names 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_from_array_names 
dask/array/tests/test_array_core.py::test_array_picklable[array0] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_array_picklable[array0] 
dask/array/tests/test_array_core.py::test_array_picklable[array1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-fftn-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-fftn-scipy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_array_picklable[array1] 
dask/array/tests/test_array_core.py::test_from_array_raises_on_bad_chunks 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_from_array_raises_on_bad_chunks 
dask/array/tests/test_array_core.py::test_concatenate_axes 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_concatenate_axes 
dask/array/tests/test_array_core.py::test_blockwise_concatenate 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-fftn-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-ifftn-numpy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-ifftn-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-ifftn-scipy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_blockwise_concatenate 
dask/array/tests/test_array_core.py::test_common_blockdim 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_common_blockdim 
dask/array/tests/test_array_core.py::test_uneven_chunks_that_fit_neatly 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-ifftn-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-rfft2-numpy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_uneven_chunks_that_fit_neatly 
dask/array/tests/test_array_core.py::test_elemwise_uneven_chunks 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-rfft2-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-rfft2-scipy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_elemwise_uneven_chunks 
dask/array/tests/test_array_core.py::test_uneven_chunks_blockwise 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-rfft2-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-irfft2-numpy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_uneven_chunks_blockwise 
dask/array/tests/test_array_core.py::test_warn_bad_rechunking 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-irfft2-numpy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_warn_bad_rechunking 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-irfft2-scipy.fft] 
dask/array/tests/test_array_core.py::test_concatenate_stack_dont_warn 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_concatenate_stack_dont_warn 
dask/array/tests/test_array_core.py::test_map_blocks_delayed 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_delayed 
dask/array/tests/test_array_core.py::test_no_chunks 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-irfft2-scipy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-rfftn-numpy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_no_chunks 
dask/array/tests/test_array_core.py::test_no_chunks_2d 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-rfftn-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-rfftn-scipy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_no_chunks_2d 
dask/array/tests/test_array_core.py::test_no_chunks_yes_chunks 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_no_chunks_yes_chunks 
dask/array/tests/test_array_core.py::test_raise_informative_errors_no_chunks 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-rfftn-scipy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_raise_informative_errors_no_chunks 
dask/array/tests/test_array_core.py::test_no_chunks_slicing_2d 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-irfftn-numpy.fft] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_no_chunks_slicing_2d 
dask/array/tests/test_array_core.py::test_index_array_with_array_1d 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_index_array_with_array_1d 
dask/array/tests/test_array_core.py::test_index_array_with_array_2d 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-irfftn-numpy.fft] 
dask/array/tests/test_fft.py::test_wrap_fftns[float64-irfftn-scipy.fft] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_wrap_fftns[float64-irfftn-scipy.fft] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-1.0-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-1.0-1] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-1.0-2] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_index_array_with_array_2d 
dask/array/tests/test_array_core.py::test_index_array_with_array_3d_2d 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-1.0-2] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-1.0-3] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-1.0-3] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-1.0-6] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-1.0-6] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-1.0-7] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-1.0-7] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-0.5-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-0.5-1] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-0.5-2] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-0.5-2] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-0.5-3] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-0.5-3] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-0.5-6] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-0.5-6] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-0.5-7] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-0.5-7] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-6.283185307179586-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-6.283185307179586-1] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-6.283185307179586-2] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-6.283185307179586-2] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-6.283185307179586-3] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-6.283185307179586-3] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-6.283185307179586-6] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-6.283185307179586-6] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-6.283185307179586-7] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>0-6.283185307179586-7] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-1.0-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-1.0-1] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-1.0-2] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-1.0-2] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-1.0-3] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-1.0-3] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-1.0-6] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-1.0-6] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-1.0-7] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-1.0-7] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-0.5-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-0.5-1] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-0.5-2] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-0.5-2] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-0.5-3] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-0.5-3] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-0.5-6] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-0.5-6] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-0.5-7] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-0.5-7] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-6.283185307179586-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-6.283185307179586-1] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-6.283185307179586-2] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-6.283185307179586-2] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-6.283185307179586-3] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-6.283185307179586-3] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-6.283185307179586-6] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-6.283185307179586-6] 
dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-6.283185307179586-7] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftfreq[<lambda>1-6.283185307179586-7] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-1.0-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-1.0-1] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-1.0-2] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-1.0-2] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-1.0-3] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-1.0-3] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-1.0-6] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-1.0-6] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-1.0-7] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-1.0-7] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-0.5-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-0.5-1] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-0.5-2] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-0.5-2] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-0.5-3] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-0.5-3] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-0.5-6] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-0.5-6] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-0.5-7] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-0.5-7] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-6.283185307179586-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-6.283185307179586-1] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-6.283185307179586-2] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-6.283185307179586-2] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-6.283185307179586-3] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-6.283185307179586-3] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-6.283185307179586-6] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-6.283185307179586-6] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-6.283185307179586-7] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>0-6.283185307179586-7] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-1.0-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-1.0-1] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-1.0-2] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-1.0-2] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-1.0-3] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-1.0-3] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-1.0-6] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-1.0-6] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-1.0-7] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-1.0-7] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-0.5-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-0.5-1] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-0.5-2] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-0.5-2] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-0.5-3] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-0.5-3] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-0.5-6] 
[gw0] [  4%] XFAIL dask/array/tests/test_array_core.py::test_index_array_with_array_3d_2d 
dask/array/tests/test_array_core.py::test_setitem_1d 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-0.5-6] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-0.5-7] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-0.5-7] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-6.283185307179586-1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-6.283185307179586-1] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-6.283185307179586-2] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_1d 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-6.283185307179586-2] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-6.283185307179586-3] 
dask/array/tests/test_array_core.py::test_setitem_masked 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-6.283185307179586-3] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-6.283185307179586-6] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_masked 
dask/array/tests/test_array_core.py::test_setitem_hardmask 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-6.283185307179586-6] 
dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-6.283185307179586-7] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_hardmask 
dask/array/tests/test_array_core.py::test_setitem_slice_twice 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_rfftfreq[<lambda>1-6.283185307179586-7] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_slice_twice 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-None-fftshift] 
dask/array/tests/test_array_core.py::test_setitem_2d 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-None-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-None-ifftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_2d 
dask/array/tests/test_array_core.py::test_setitem_extended_API_0d 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_0d 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index0--1] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index0--1] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index1--2] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-None-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-0-fftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index1--2] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index2--3] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index2--3] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index3-value3] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index3-value3] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index4--4] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-0-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-0-ifftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index4--4] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index5-value5] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index5-value5] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index6--5] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-0-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-1-fftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index6--5] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index7--6] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index7--6] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index8--4] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-1-fftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index8--4] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-1-ifftshift] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index9--5] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index9--5] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index10-value10] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-1-ifftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index10-value10] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-2-fftshift] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index11-value11] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_1d[index11-value11] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index0--1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-2-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-2-ifftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index0--1] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index1--1] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index1--1] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index2--1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-2-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes4-fftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index2--1] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index3--1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes4-fftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index3--1] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index4--1] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes4-ifftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index4--1] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[5--1] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes4-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes5-fftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[5--1] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index6-value6] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes5-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes5-ifftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index6-value6] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[3-value7] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[3-value7] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index8-value8] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes5-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes6-fftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index8-value8] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes6-fftshift] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index9-value9] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes6-ifftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index9-value9] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index10-value10] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes6-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes7-fftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index10-value10] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index11-value11] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes7-fftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index11-value11] 
dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes7-ifftshift] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index12-value12] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index12-value12] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index13-value13] 
[gw1] [  4%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape0-chunks0-axes7-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-None-fftshift] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index13-value13] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index14--1] 
[gw0] [  4%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index14--1] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index15--1] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-None-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-None-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index15--1] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index16--1] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index16--1] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index17--1] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-None-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-0-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-0-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-0-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-0-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-1-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index17--1] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index18-value18] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-1-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-1-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-1-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-2-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-2-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-2-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-2-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes4-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index18-value18] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index19--99] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes4-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes4-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index19--99] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index20-value20] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes4-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes5-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index20-value20] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index21--98] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index21--98] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index22-value22] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes5-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes5-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d[index22-value22] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d_rhs_func_of_lhs 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes5-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes6-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes6-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes6-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes6-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes7-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes7-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes7-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape1-chunks1-axes7-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-None-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-None-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-None-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-None-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-0-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-0-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-0-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-0-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-1-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-1-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-1-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-1-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-2-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-2-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-2-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-2-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes4-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes4-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes4-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes4-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes5-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes5-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes5-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes5-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes6-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes6-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes6-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes6-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes7-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes7-fftshift] 
dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes7-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift[shape2-chunks2-axes7-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-None-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-None-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-None-ifftshift-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-None-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-0-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-0-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-0-ifftshift-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-0-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-1-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-1-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-1-ifftshift-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-1-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-2-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-2-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-2-ifftshift-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-2-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes4-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes4-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes4-ifftshift-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes4-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes5-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes5-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes5-ifftshift-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes5-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes6-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes6-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes6-ifftshift-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes6-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes7-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes7-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes7-ifftshift-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape0-chunks0-axes7-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-None-fftshift-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d_rhs_func_of_lhs 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d_mask[index0-value0] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d_mask[index0-value0] 
dask/array/tests/test_array_core.py::test_setitem_extended_API_2d_mask[index1-value1] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-None-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-None-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_extended_API_2d_mask[index1-value1] 
dask/array/tests/test_array_core.py::test_setitem_on_read_only_blocks 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_on_read_only_blocks 
dask/array/tests/test_array_core.py::test_setitem_errs 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-None-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-0-fftshift-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_errs 
dask/array/tests/test_array_core.py::test_setitem_bool_index_errs[numpy] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-0-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-0-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_bool_index_errs[numpy] 
dask/array/tests/test_array_core.py::test_setitem_bool_index_errs[dask.array] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-0-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-1-fftshift-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_setitem_bool_index_errs[dask.array] 
dask/array/tests/test_array_core.py::test_zero_slice_dtypes 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zero_slice_dtypes 
dask/array/tests/test_array_core.py::test_zero_sized_array_rechunk 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zero_sized_array_rechunk 
dask/array/tests/test_array_core.py::test_blockwise_zero_shape 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_blockwise_zero_shape 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-1-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-1-ifftshift-fftshift] 
dask/array/tests/test_array_core.py::test_blockwise_zero_shape_new_axes 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_blockwise_zero_shape_new_axes 
dask/array/tests/test_array_core.py::test_broadcast_against_zero_shape 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-1-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-2-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-2-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-2-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_broadcast_against_zero_shape 
dask/array/tests/test_array_core.py::test_from_array_name 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_from_array_name 
dask/array/tests/test_array_core.py::test_concatenate_errs 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-2-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_concatenate_errs 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes4-fftshift-ifftshift] 
dask/array/tests/test_array_core.py::test_stack_errs 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_stack_errs 
dask/array/tests/test_array_core.py::test_blockwise_with_numpy_arrays 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_blockwise_with_numpy_arrays 
dask/array/tests/test_array_core.py::test_elemwise_with_lists[other0-100] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_elemwise_with_lists[other0-100] 
dask/array/tests/test_array_core.py::test_elemwise_with_lists[other0-6] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes4-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes4-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_elemwise_with_lists[other0-6] 
dask/array/tests/test_array_core.py::test_elemwise_with_lists[other1-100] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_elemwise_with_lists[other1-100] 
dask/array/tests/test_array_core.py::test_elemwise_with_lists[other1-6] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes4-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes5-fftshift-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_elemwise_with_lists[other1-6] 
dask/array/tests/test_array_core.py::test_elemwise_with_lists[other2-100] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_elemwise_with_lists[other2-100] 
dask/array/tests/test_array_core.py::test_elemwise_with_lists[other2-6] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_elemwise_with_lists[other2-6] 
dask/array/tests/test_array_core.py::test_constructor_plugin 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_constructor_plugin 
dask/array/tests/test_array_core.py::test_no_warnings_on_metadata 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_no_warnings_on_metadata 
dask/array/tests/test_array_core.py::test_delayed_array_key_hygeine 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_delayed_array_key_hygeine 
dask/array/tests/test_array_core.py::test_empty_chunks_in_array_len 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_empty_chunks_in_array_len 
dask/array/tests/test_array_core.py::test_meta[None] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_meta[None] 
dask/array/tests/test_array_core.py::test_meta[dtype1] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_meta[dtype1] 
dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[100-10-expected0] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[100-10-expected0] 
dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[20-10-expected1] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[20-10-expected1] 
dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[20-5-expected2] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[20-5-expected2] 
dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[24-5-expected3] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[24-5-expected3] 
dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[23-5-expected4] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[23-5-expected4] 
dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[1000-167-expected5] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_auto_1d[1000-167-expected5] 
dask/array/tests/test_array_core.py::test_normalize_chunks_auto_2d[shape0-chunks0-20-expected0] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_auto_2d[shape0-chunks0-20-expected0] 
dask/array/tests/test_array_core.py::test_normalize_chunks_auto_2d[shape1-chunks1-20-expected1] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_auto_2d[shape1-chunks1-20-expected1] 
dask/array/tests/test_array_core.py::test_normalize_chunks_auto_2d[shape2-auto-10-expected2] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_auto_2d[shape2-auto-10-expected2] 
dask/array/tests/test_array_core.py::test_normalize_chunks_auto_3d 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_auto_3d 
dask/array/tests/test_array_core.py::test_constructors_chunks_dict 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_constructors_chunks_dict 
dask/array/tests/test_array_core.py::test_from_array_chunks_dict 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_from_array_chunks_dict 
dask/array/tests/test_array_core.py::test_normalize_chunks_object_dtype[object] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_object_dtype[object] 
dask/array/tests/test_array_core.py::test_normalize_chunks_object_dtype[dtype1] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_object_dtype[dtype1] 
dask/array/tests/test_array_core.py::test_normalize_chunks_tuples_of_tuples 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_tuples_of_tuples 
dask/array/tests/test_array_core.py::test_normalize_chunks_nan 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_normalize_chunks_nan 
dask/array/tests/test_array_core.py::test_pandas_from_dask_array 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes5-fftshift-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_pandas_from_dask_array 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes5-ifftshift-fftshift] 
dask/array/tests/test_array_core.py::test_from_zarr_unique_name 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_from_zarr_unique_name 
dask/array/tests/test_array_core.py::test_from_zarr_name 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_from_zarr_name 
dask/array/tests/test_array_core.py::test_zarr_roundtrip 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes5-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes6-fftshift-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_roundtrip 
dask/array/tests/test_array_core.py::test_zarr_roundtrip_with_path_like 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes6-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes6-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_roundtrip_with_path_like 
dask/array/tests/test_array_core.py::test_to_zarr_accepts_empty_array_without_exception_raised 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_to_zarr_accepts_empty_array_without_exception_raised 
dask/array/tests/test_array_core.py::test_zarr_return_stored[False] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes6-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes7-fftshift-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_return_stored[False] 
dask/array/tests/test_array_core.py::test_zarr_return_stored[True] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_return_stored[True] 
dask/array/tests/test_array_core.py::test_zarr_inline_array[True] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_inline_array[True] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes7-fftshift-ifftshift] 
dask/array/tests/test_array_core.py::test_zarr_inline_array[False] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes7-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_inline_array[False] 
dask/array/tests/test_array_core.py::test_zarr_existing_array 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_existing_array 
dask/array/tests/test_array_core.py::test_to_zarr_unknown_chunks_raises 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_to_zarr_unknown_chunks_raises 
dask/array/tests/test_array_core.py::test_read_zarr_chunks 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_read_zarr_chunks 
dask/array/tests/test_array_core.py::test_zarr_pass_store 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape1-chunks1-axes7-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-None-fftshift-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_pass_store 
dask/array/tests/test_array_core.py::test_zarr_group 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-None-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-None-ifftshift-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-None-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-0-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-0-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-0-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_group 
dask/array/tests/test_array_core.py::test_zarr_irregular_chunks[shape0-chunks0-True] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-0-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-1-fftshift-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_irregular_chunks[shape0-chunks0-True] 
dask/array/tests/test_array_core.py::test_zarr_irregular_chunks[shape1-chunks1-True] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-1-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-1-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_irregular_chunks[shape1-chunks1-True] 
dask/array/tests/test_array_core.py::test_zarr_irregular_chunks[shape2-chunks2-False] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-1-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-2-fftshift-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_irregular_chunks[shape2-chunks2-False] 
dask/array/tests/test_array_core.py::test_zarr_irregular_chunks[shape3-chunks3-False] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-2-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-2-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_irregular_chunks[shape3-chunks3-False] 
dask/array/tests/test_array_core.py::test_zarr_irregular_chunks[shape4-chunks4-True] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-2-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes4-fftshift-ifftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_irregular_chunks[shape4-chunks4-True] 
dask/array/tests/test_array_core.py::test_regular_chunks[data0] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_regular_chunks[data0] 
dask/array/tests/test_array_core.py::test_regular_chunks[data1] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_regular_chunks[data1] 
dask/array/tests/test_array_core.py::test_regular_chunks[data2] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_regular_chunks[data2] 
dask/array/tests/test_array_core.py::test_regular_chunks[data3] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_regular_chunks[data3] 
dask/array/tests/test_array_core.py::test_regular_chunks[data4] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_regular_chunks[data4] 
dask/array/tests/test_array_core.py::test_regular_chunks[data5] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_regular_chunks[data5] 
dask/array/tests/test_array_core.py::test_regular_chunks[data6] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_regular_chunks[data6] 
dask/array/tests/test_array_core.py::test_regular_chunks[data7] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_regular_chunks[data7] 
dask/array/tests/test_array_core.py::test_zarr_nocompute 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes4-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes4-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_nocompute 
dask/array/tests/test_array_core.py::test_zarr_regions 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes4-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes5-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes5-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes5-ifftshift-fftshift] 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_zarr_regions 
dask/array/tests/test_array_core.py::test_tiledb_roundtrip 
[gw0] [  5%] SKIPPED dask/array/tests/test_array_core.py::test_tiledb_roundtrip 
dask/array/tests/test_array_core.py::test_tiledb_multiattr 
[gw0] [  5%] SKIPPED dask/array/tests/test_array_core.py::test_tiledb_multiattr 
dask/array/tests/test_array_core.py::test_blockview 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes5-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes6-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes6-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes6-ifftshift-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes6-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes7-fftshift-ifftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes7-fftshift-ifftshift] 
dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes7-ifftshift-fftshift] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_fftshift_identity[shape2-chunks2-axes7-ifftshift-fftshift] 
dask/array/tests/test_fft.py::test_scipy_fftpack_future_warning[numpy.fft] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_scipy_fftpack_future_warning[numpy.fft] 
dask/array/tests/test_fft.py::test_scipy_fftpack_future_warning[scipy.fft] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_scipy_fftpack_future_warning[scipy.fft] 
dask/array/tests/test_fft.py::test_scipy_fftpack_future_warning[scipy.fftpack] 
[gw1] [  5%] PASSED dask/array/tests/test_fft.py::test_scipy_fftpack_future_warning[scipy.fftpack] 
dask/array/tests/test_gufunc.py::test__parse_gufunc_signature 
[gw1] [  5%] PASSED dask/array/tests/test_gufunc.py::test__parse_gufunc_signature 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_input_validation_01 
[gw1] [  5%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_input_validation_01 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_args_validation 
[gw1] [  5%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_args_validation 
dask/array/tests/test_gufunc.py::test__validate_normalize_axes_01 
[gw1] [  5%] PASSED dask/array/tests/test_gufunc.py::test__validate_normalize_axes_01 
dask/array/tests/test_gufunc.py::test__validate_normalize_axes_02 
[gw1] [  5%] PASSED dask/array/tests/test_gufunc.py::test__validate_normalize_axes_02 
dask/array/tests/test_gufunc.py::test__validate_normalize_axes_03 
[gw1] [  5%] PASSED dask/array/tests/test_gufunc.py::test__validate_normalize_axes_03 
dask/array/tests/test_gufunc.py::test_apply_gufunc_01 
[gw1] [  5%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_01 
dask/array/tests/test_gufunc.py::test_apply_gufunc_01b 
[gw0] [  5%] PASSED dask/array/tests/test_array_core.py::test_blockview 
dask/array/tests/test_array_core.py::test_blocks_indexer 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_01b 
dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes_string[False] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes_string[False] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes_string[True] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes_string[True] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes_string_many_outputs[False] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes_string_many_outputs[False] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes_string_many_outputs[True] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_blocks_indexer 
dask/array/tests/test_array_core.py::test_partitions_indexer 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes_string_many_outputs[True] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_pass_additional_kwargs 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_pass_additional_kwargs 
dask/array/tests/test_gufunc.py::test_apply_gufunc_02 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_02 
dask/array/tests/test_gufunc.py::test_apply_gufunc_scalar_output 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_scalar_output 
dask/array/tests/test_gufunc.py::test_apply_gufunc_elemwise_01 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_elemwise_01 
dask/array/tests/test_gufunc.py::test_apply_gufunc_elemwise_01b 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_elemwise_01b 
dask/array/tests/test_gufunc.py::test_apply_gufunc_elemwise_02 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_elemwise_02 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_partitions_indexer 
dask/array/tests/test_gufunc.py::test_gufunc_vector_output 
dask/array/tests/test_array_core.py::test_dask_array_holds_scipy_sparse_containers[array] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_gufunc_vector_output 
dask/array/tests/test_gufunc.py::test_apply_gufunc_elemwise_loop 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_elemwise_loop 
dask/array/tests/test_gufunc.py::test_apply_gufunc_elemwise_core 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_elemwise_core 
dask/array/tests/test_gufunc.py::test_apply_gufunc_two_scalar_output 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_two_scalar_output 
dask/array/tests/test_gufunc.py::test_apply_gufunc_two_mixed_outputs 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_two_mixed_outputs 
dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes[int] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_dask_array_holds_scipy_sparse_containers[array] 
dask/array/tests/test_array_core.py::test_dask_array_holds_scipy_sparse_containers[matrix] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes[int] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes[output_dtypes1] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_output_dtypes[output_dtypes1] 
dask/array/tests/test_gufunc.py::test_gufunc_two_inputs 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_gufunc_two_inputs 
dask/array/tests/test_gufunc.py::test_gufunc_mixed_inputs 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_dask_array_holds_scipy_sparse_containers[matrix] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-index0] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_gufunc_mixed_inputs 
dask/array/tests/test_gufunc.py::test_gufunc_mixed_inputs_vectorize 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_gufunc_mixed_inputs_vectorize 
dask/array/tests/test_gufunc.py::test_gufunc_vectorize_whitespace 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-index0] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-0] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_gufunc_vectorize_whitespace 
dask/array/tests/test_gufunc.py::test_gufunc 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-0] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-index2] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_gufunc 
dask/array/tests/test_gufunc.py::test_as_gufunc 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-index2] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-index3] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_as_gufunc 
dask/array/tests/test_gufunc.py::test_apply_gufunc_broadcasting_loopdims 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-index3] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-index4] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-index4] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-index5] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_broadcasting_loopdims 
dask/array/tests/test_gufunc.py::test_apply_gufunc_check_same_dimsizes 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_check_same_dimsizes 
dask/array/tests/test_gufunc.py::test_apply_gufunc_check_coredim_chunksize 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_check_coredim_chunksize 
dask/array/tests/test_gufunc.py::test_apply_gufunc_check_inhomogeneous_chunksize 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_check_inhomogeneous_chunksize 
dask/array/tests/test_gufunc.py::test_apply_gufunc_infer_dtype 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_matrix-index5] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-index0] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-index0] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-0] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-0] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-index2] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_infer_dtype 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_01[False] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-index2] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-index3] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-index3] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-index4] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_01[False] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_01[True] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_01[True] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_02 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-index4] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-index5] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_02 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_02b 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[scipy.sparse-csr_array-index5] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-index0] 
[gw0] [  6%] SKIPPED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-index0] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-0] 
[gw0] [  6%] SKIPPED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-0] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-index2] 
[gw0] [  6%] SKIPPED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-index2] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-index3] 
[gw0] [  6%] SKIPPED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-index3] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-index4] 
[gw0] [  6%] SKIPPED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-index4] 
dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-index5] 
[gw0] [  6%] SKIPPED dask/array/tests/test_array_core.py::test_scipy_sparse_indexing[cupyx.scipy.sparse-csr_matrix-index5] 
dask/array/tests/test_array_core.py::test_scipy_sparse_concatenate[array-0] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_02b 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_03 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_03 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_keepdims[-2] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_keepdims[-2] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_concatenate[array-0] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_keepdims[-1] 
dask/array/tests/test_array_core.py::test_scipy_sparse_concatenate[array-1] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_keepdims[-1] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_keepdims[None] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axis_keepdims[None] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_01[axes0] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_concatenate[array-1] 
dask/array/tests/test_array_core.py::test_scipy_sparse_concatenate[matrix-0] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_01[axes0] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_01[axes1] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_01[axes1] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_concatenate[matrix-0] 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_02 
dask/array/tests/test_array_core.py::test_scipy_sparse_concatenate[matrix-1] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_02 
dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_two_kept_coredims 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_axes_two_kept_coredims 
dask/array/tests/test_gufunc.py::test_apply_gufunc_via_numba_01 
[gw1] [  6%] SKIPPED dask/array/tests/test_gufunc.py::test_apply_gufunc_via_numba_01 
dask/array/tests/test_gufunc.py::test_apply_gufunc_via_numba_02 
[gw1] [  6%] SKIPPED dask/array/tests/test_gufunc.py::test_apply_gufunc_via_numba_02 
dask/array/tests/test_gufunc.py::test_preserve_meta_type 
[gw1] [  6%] SKIPPED dask/array/tests/test_gufunc.py::test_preserve_meta_type 
dask/array/tests/test_gufunc.py::test_apply_gufunc_with_meta 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_concatenate[matrix-1] 
dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src0-asarray] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src0-asarray] 
dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src0-asanyarray] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_apply_gufunc_with_meta 
dask/array/tests/test_gufunc.py::test_as_gufunc_with_meta 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src0-asanyarray] 
dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src1-asarray] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src1-asarray] 
dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src1-asanyarray] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_as_gufunc_with_meta 
dask/array/tests/test_gufunc.py::test_gufunc_chunksizes_adjustment 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src1-asanyarray] 
dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src2-asarray] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src2-asarray] 
dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src2-asanyarray] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scipy_sparse_asarray_like[src2-asanyarray] 
[gw1] [  6%] PASSED dask/array/tests/test_gufunc.py::test_gufunc_chunksizes_adjustment 
dask/array/tests/test_array_core.py::test_3851 
dask/array/tests/test_linalg.py::test_tsqr[20-10-10-None] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_3851 
dask/array/tests/test_array_core.py::test_3925 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_3925 
dask/array/tests/test_array_core.py::test_map_blocks_large_inputs_delayed 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_large_inputs_delayed 
dask/array/tests/test_array_core.py::test_blockwise_large_inputs_delayed 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_blockwise_large_inputs_delayed 
dask/array/tests/test_array_core.py::test_slice_reversed 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_slice_reversed 
dask/array/tests/test_array_core.py::test_map_blocks_chunks 
[gw1] [  6%] PASSED dask/array/tests/test_linalg.py::test_tsqr[20-10-10-None] 
dask/array/tests/test_linalg.py::test_tsqr[20-10-chunks1-None] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_map_blocks_chunks 
dask/array/tests/test_array_core.py::test_nbytes_auto 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_nbytes_auto 
dask/array/tests/test_array_core.py::test_auto_chunks 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_auto_chunks 
dask/array/tests/test_array_core.py::test_auto_chunks_h5py 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_auto_chunks_h5py 
dask/array/tests/test_array_core.py::test_no_warnings_from_blockwise 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_no_warnings_from_blockwise 
dask/array/tests/test_array_core.py::test_from_array_meta 
[gw0] [  6%] SKIPPED dask/array/tests/test_array_core.py::test_from_array_meta 
dask/array/tests/test_array_core.py::test_compute_chunk_sizes 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_compute_chunk_sizes 
dask/array/tests/test_array_core.py::test_compute_chunk_sizes_2d_array 
[gw1] [  6%] PASSED dask/array/tests/test_linalg.py::test_tsqr[20-10-chunks1-None] 
dask/array/tests/test_linalg.py::test_tsqr[20-10-chunks2-None] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_compute_chunk_sizes_2d_array 
dask/array/tests/test_array_core.py::test_compute_chunk_sizes_3d_array 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_compute_chunk_sizes_3d_array 
dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_rechunk 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_rechunk 
dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_to_zarr 
[gw1] [  6%] PASSED dask/array/tests/test_linalg.py::test_tsqr[20-10-chunks2-None] 
dask/array/tests/test_linalg.py::test_tsqr[40-10-chunks3-None] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_to_zarr 
dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_to_svg 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_to_svg 
dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_concatenate 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_concatenate 
dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_reduction 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_reduction 
[gw1] [  6%] PASSED dask/array/tests/test_linalg.py::test_tsqr[40-10-chunks3-None] 
dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_reshape 
dask/array/tests/test_linalg.py::test_tsqr[128-2-chunks4-None] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_reshape 
dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_slicing 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_compute_chunk_sizes_warning_fixes_slicing 
dask/array/tests/test_array_core.py::test_rechunk_auto 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_rechunk_auto 
dask/array/tests/test_array_core.py::test_chunk_assignment_invalidates_cached_properties 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_chunk_assignment_invalidates_cached_properties 
dask/array/tests/test_array_core.py::test_map_blocks_series 
[gw0] [  6%] SKIPPED dask/array/tests/test_array_core.py::test_map_blocks_series 
dask/array/tests/test_array_core.py::test_map_blocks_dataframe 
[gw1] [  6%] PASSED dask/array/tests/test_linalg.py::test_tsqr[128-2-chunks4-None] 
dask/array/tests/test_linalg.py::test_tsqr[129-2-chunks5-None] 
[gw0] [  6%] XFAIL dask/array/tests/test_array_core.py::test_map_blocks_dataframe 
dask/array/tests/test_array_core.py::test_dask_layers 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_dask_layers 
dask/array/tests/test_array_core.py::test_len_object_with_unknown_size 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_len_object_with_unknown_size 
dask/array/tests/test_array_core.py::test_chunk_shape_broadcast[0] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_chunk_shape_broadcast[0] 
dask/array/tests/test_array_core.py::test_chunk_shape_broadcast[1] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_chunk_shape_broadcast[1] 
dask/array/tests/test_array_core.py::test_chunk_shape_broadcast[3] 
[gw1] [  6%] PASSED dask/array/tests/test_linalg.py::test_tsqr[129-2-chunks5-None] 
dask/array/tests/test_linalg.py::test_tsqr[130-2-chunks6-None] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_chunk_shape_broadcast[3] 
dask/array/tests/test_array_core.py::test_chunk_shape_broadcast[8] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_chunk_shape_broadcast[8] 
dask/array/tests/test_array_core.py::test_chunk_non_array_like 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_chunk_non_array_like 
dask/array/tests/test_array_core.py::test_to_backend 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_to_backend 
dask/array/tests/test_array_core.py::test_from_array_copies 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_from_array_copies 
dask/array/tests/test_array_core.py::test_from_array_xarray_dataarray 
[gw0] [  6%] SKIPPED dask/array/tests/test_array_core.py::test_from_array_xarray_dataarray 
dask/array/tests/test_array_core.py::test_load_store_chunk 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_load_store_chunk 
dask/array/tests/test_array_core.py::test_scalar_setitem 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_scalar_setitem 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_0-idx0] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_0-idx0] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_0-idx1] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_0-idx1] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_0-idx2] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_0-idx2] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_0-idx3] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_0-idx3] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_1-idx0] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_1-idx0] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_1-idx1] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_1-idx1] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_1-idx2] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_1-idx2] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_1-idx3] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[3.3_1-idx3] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val2-idx0] 
[gw1] [  6%] PASSED dask/array/tests/test_linalg.py::test_tsqr[130-2-chunks6-None] 
dask/array/tests/test_linalg.py::test_tsqr[131-2-chunks7-None] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val2-idx0] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val2-idx1] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val2-idx1] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val2-idx2] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val2-idx2] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val2-idx3] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val2-idx3] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val3-idx0] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val3-idx0] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val3-idx1] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val3-idx1] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val3-idx2] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val3-idx2] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val3-idx3] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val3-idx3] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val4-idx0] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val4-idx0] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val4-idx1] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val4-idx1] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val4-idx2] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val4-idx2] 
dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val4-idx3] 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_setitem_no_dtype_broadcast[val4-idx3] 
dask/array/tests/test_array_core.py::test_store_sources_unoptimized_nocompute 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_store_sources_unoptimized_nocompute 
dask/array/tests/test_array_core.py::test_blockwise_fusion 
[gw0] [  6%] PASSED dask/array/tests/test_array_core.py::test_blockwise_fusion 
[gw1] [  6%] PASSED dask/array/tests/test_linalg.py::test_tsqr[131-2-chunks7-None] 
dask/array/tests/test_array_expr_incompatible.py::test_concatenate3_nep18_dispatching[True] 
[gw0] [  6%] PASSED dask/array/tests/test_array_expr_incompatible.py::test_concatenate3_nep18_dispatching[True] 
dask/array/tests/test_array_expr_incompatible.py::test_concatenate3_nep18_dispatching[False] 
dask/array/tests/test_linalg.py::test_tsqr[300-10-chunks8-None] 
[gw0] [  6%] PASSED dask/array/tests/test_array_expr_incompatible.py::test_concatenate3_nep18_dispatching[False] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>0] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>0] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>1] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>1] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>2] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>2] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>3] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>3] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>4] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>4] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>5] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>5] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>6] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>6] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>7] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>7] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>8] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>8] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>9] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>9] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>10] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>10] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>11] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>11] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>12] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>12] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>13] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>13] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>14] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>14] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>15] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>15] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>16] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>16] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>17] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>17] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>18] 
[gw1] [  6%] PASSED dask/array/tests/test_linalg.py::test_tsqr[300-10-chunks8-None] 
dask/array/tests/test_linalg.py::test_tsqr[300-10-chunks9-None] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>18] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>19] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>19] 
dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>20] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_dask[<lambda>20] 
dask/array/tests/test_array_function.py::test_stack_functions_require_sequence_of_arrays[<lambda>0] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_stack_functions_require_sequence_of_arrays[<lambda>0] 
dask/array/tests/test_array_function.py::test_stack_functions_require_sequence_of_arrays[<lambda>1] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_stack_functions_require_sequence_of_arrays[<lambda>1] 
dask/array/tests/test_array_function.py::test_stack_functions_require_sequence_of_arrays[<lambda>2] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_stack_functions_require_sequence_of_arrays[<lambda>2] 
dask/array/tests/test_array_function.py::test_array_function_fft[fft] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_fft[fft] 
dask/array/tests/test_array_function.py::test_array_function_fft[fft2] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_function_fft[fft2] 
dask/array/tests/test_array_function.py::test_array_notimpl_function_dask[<lambda>0] 
[gw0] [  6%] PASSED dask/array/tests/test_array_function.py::test_array_notimpl_function_dask[<lambda>0] 
dask/array/tests/test_array_function.py::test_array_notimpl_function_dask[<lambda>1] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_array_notimpl_function_dask[<lambda>1] 
dask/array/tests/test_array_function.py::test_array_notimpl_function_dask[<lambda>2] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_array_notimpl_function_dask[<lambda>2] 
dask/array/tests/test_array_function.py::test_array_function_sparse[<lambda>0] 
[gw0] [  7%] SKIPPED dask/array/tests/test_array_function.py::test_array_function_sparse[<lambda>0] 
dask/array/tests/test_array_function.py::test_array_function_sparse[<lambda>1] 
[gw0] [  7%] SKIPPED dask/array/tests/test_array_function.py::test_array_function_sparse[<lambda>1] 
dask/array/tests/test_array_function.py::test_array_function_sparse[<lambda>2] 
[gw0] [  7%] SKIPPED dask/array/tests/test_array_function.py::test_array_function_sparse[<lambda>2] 
dask/array/tests/test_array_function.py::test_array_function_sparse_tensordot 
[gw0] [  7%] SKIPPED dask/array/tests/test_array_function.py::test_array_function_sparse_tensordot 
dask/array/tests/test_array_function.py::test_array_function_cupy_svd[chunks0] 
[gw0] [  7%] SKIPPED dask/array/tests/test_array_function.py::test_array_function_cupy_svd[chunks0] 
dask/array/tests/test_array_function.py::test_array_function_cupy_svd[chunks1] 
[gw0] [  7%] SKIPPED dask/array/tests/test_array_function.py::test_array_function_cupy_svd[chunks1] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>0] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>0] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>1] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>1] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>2] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr[300-10-chunks9-None] 
dask/array/tests/test_linalg.py::test_tsqr[300-10-chunks10-None] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>2] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>3] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>3] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>4] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>4] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>5] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>5] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>6] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>6] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>7] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>7] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>8] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>8] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>9] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>9] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>10] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>10] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>11] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>11] 
dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>12] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_unregistered_func[<lambda>12] 
dask/array/tests/test_array_function.py::test_non_existent_func 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_non_existent_func 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast0-arr_downcast0-equal] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast0-arr_downcast0-equal] 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast0-arr_downcast0-matmul] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast0-arr_downcast0-matmul] 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast0-arr_downcast0-dot] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast0-arr_downcast0-dot] 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast0-arr_downcast0-<lambda>] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast0-arr_downcast0-<lambda>] 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast1-arr_downcast1-equal] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast1-arr_downcast1-equal] 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast1-arr_downcast1-matmul] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast1-arr_downcast1-matmul] 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast1-arr_downcast1-dot] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast1-arr_downcast1-dot] 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast1-arr_downcast1-<lambda>] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast1-arr_downcast1-<lambda>] 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast2-arr_downcast2-equal] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast2-arr_downcast2-equal] 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast2-arr_downcast2-matmul] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast2-arr_downcast2-matmul] 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast2-arr_downcast2-dot] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast2-arr_downcast2-dot] 
dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast2-arr_downcast2-<lambda>] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_binary_function_type_precedence[arr_upcast2-arr_downcast2-<lambda>] 
dask/array/tests/test_array_function.py::test_like[array-numpy] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like[array-numpy] 
dask/array/tests/test_array_function.py::test_like[array-dask.array] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like[array-dask.array] 
dask/array/tests/test_array_function.py::test_like[asarray-numpy] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr[300-10-chunks10-None] 
dask/array/tests/test_linalg.py::test_tsqr[10-5-10-None] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like[asarray-numpy] 
dask/array/tests/test_array_function.py::test_like[asarray-dask.array] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr[10-5-10-None] 
dask/array/tests/test_linalg.py::test_tsqr[5-10-10-None] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like[asarray-dask.array] 
dask/array/tests/test_array_function.py::test_like[asanyarray-numpy] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like[asanyarray-numpy] 
dask/array/tests/test_array_function.py::test_like[asanyarray-dask.array] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr[5-10-10-None] 
dask/array/tests/test_linalg.py::test_tsqr[10-10-10-None] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like[asanyarray-dask.array] 
dask/array/tests/test_array_function.py::test_tri_like[numpy] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr[10-10-10-None] 
dask/array/tests/test_linalg.py::test_tsqr[10-40-chunks14-ValueError] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_tri_like[numpy] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr[10-40-chunks14-ValueError] 
dask/array/tests/test_array_function.py::test_tri_like[dask.array] 
dask/array/tests/test_linalg.py::test_tsqr[10-40-chunks15-ValueError] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr[10-40-chunks15-ValueError] 
dask/array/tests/test_linalg.py::test_tsqr[10-40-chunks16-ValueError] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr[10-40-chunks16-ValueError] 
dask/array/tests/test_linalg.py::test_tsqr[20-20-10-ValueError] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr[20-20-10-ValueError] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[10-5-chunks0-True-False-None] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_tri_like[dask.array] 
dask/array/tests/test_array_function.py::test_like_with_numpy_func[array] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like_with_numpy_func[array] 
dask/array/tests/test_array_function.py::test_like_with_numpy_func[asarray] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like_with_numpy_func[asarray] 
dask/array/tests/test_array_function.py::test_like_with_numpy_func[asanyarray] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like_with_numpy_func[asanyarray] 
dask/array/tests/test_array_function.py::test_like_with_numpy_func_and_dtype[array] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like_with_numpy_func_and_dtype[array] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[10-5-chunks0-True-False-None] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[10-5-chunks1-False-True-None] 
dask/array/tests/test_array_function.py::test_like_with_numpy_func_and_dtype[asarray] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like_with_numpy_func_and_dtype[asarray] 
dask/array/tests/test_array_function.py::test_like_with_numpy_func_and_dtype[asanyarray] 
[gw0] [  7%] PASSED dask/array/tests/test_array_function.py::test_like_with_numpy_func_and_dtype[asanyarray] 
dask/array/tests/test_array_utils.py::test_meta_from_array[asarray] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array[asarray] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-0] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-0] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-str0] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-str0] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-1] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-1] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-str1] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-str1] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-2] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-2] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-str2] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[None-str2] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-0] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-0] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-str0] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-str0] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-1] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-1] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-str1] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-str1] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-2] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-2] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[10-5-chunks1-False-True-None] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-str2] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[bool-str2] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[10-5-chunks2-True-True-None] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-0] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-0] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-str0] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-str0] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-1] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-1] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-str1] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-str1] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-2] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-2] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-str2] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[int-str2] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-0] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-0] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-str0] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-str0] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-1] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-1] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-str1] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-str1] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-2] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-2] 
dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-str2] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_literal[float-str2] 
dask/array/tests/test_array_utils.py::test_meta_from_array_type_inputs 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_type_inputs 
dask/array/tests/test_array_utils.py::test_meta_from_array_scipy_sparse 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_meta_from_array_scipy_sparse 
dask/array/tests/test_array_utils.py::test_assert_eq_checks_dtype[a0-1.0] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_assert_eq_checks_dtype[a0-1.0] 
dask/array/tests/test_array_utils.py::test_assert_eq_checks_dtype[a1-b1] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[10-5-chunks2-True-True-None] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[40-5-chunks3-True-False-None] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_assert_eq_checks_dtype[a1-b1] 
dask/array/tests/test_array_utils.py::test_assert_eq_checks_dtype[a2-b2] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_assert_eq_checks_dtype[a2-b2] 
dask/array/tests/test_array_utils.py::test_assert_eq_scheduler[1.0-1.0] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_assert_eq_scheduler[1.0-1.0] 
dask/array/tests/test_array_utils.py::test_assert_eq_scheduler[a1-b1] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_assert_eq_scheduler[a1-b1] 
dask/array/tests/test_array_utils.py::test_assert_eq_scheduler[a2-b2] 
[gw0] [  7%] PASSED dask/array/tests/test_array_utils.py::test_assert_eq_scheduler[a2-b2] 
dask/array/tests/test_atop.py::test_rewrite[inputs0-expected0] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs0-expected0] 
dask/array/tests/test_atop.py::test_rewrite[inputs1-expected1] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs1-expected1] 
dask/array/tests/test_atop.py::test_rewrite[inputs2-expected2] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs2-expected2] 
dask/array/tests/test_atop.py::test_rewrite[inputs3-expected3] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs3-expected3] 
dask/array/tests/test_atop.py::test_rewrite[inputs4-expected4] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs4-expected4] 
dask/array/tests/test_atop.py::test_rewrite[inputs5-expected5] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs5-expected5] 
dask/array/tests/test_atop.py::test_rewrite[inputs6-expected6] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs6-expected6] 
dask/array/tests/test_atop.py::test_rewrite[inputs7-expected7] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs7-expected7] 
dask/array/tests/test_atop.py::test_rewrite[inputs8-expected8] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs8-expected8] 
dask/array/tests/test_atop.py::test_rewrite[inputs9-expected9] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs9-expected9] 
dask/array/tests/test_atop.py::test_rewrite[inputs10-expected10] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs10-expected10] 
dask/array/tests/test_atop.py::test_rewrite[inputs11-expected11] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs11-expected11] 
dask/array/tests/test_atop.py::test_rewrite[inputs12-expected12] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs12-expected12] 
dask/array/tests/test_atop.py::test_rewrite[inputs13-expected13] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs13-expected13] 
dask/array/tests/test_atop.py::test_rewrite[inputs14-expected14] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs14-expected14] 
dask/array/tests/test_atop.py::test_rewrite[inputs15-expected15] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs15-expected15] 
dask/array/tests/test_atop.py::test_rewrite[inputs16-expected16] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs16-expected16] 
dask/array/tests/test_atop.py::test_rewrite[inputs17-expected17] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_rewrite[inputs17-expected17] 
dask/array/tests/test_atop.py::test_index_subs 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_index_subs 
dask/array/tests/test_atop.py::test_optimize_blockwise 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_optimize_blockwise 
dask/array/tests/test_atop.py::test_optimize_blockwise_control_annotations 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[40-5-chunks3-True-False-None] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[40-5-chunks4-False-True-None] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_optimize_blockwise_control_annotations 
dask/array/tests/test_atop.py::test_optimize_blockwise_custom_annotations 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_optimize_blockwise_custom_annotations 
dask/array/tests/test_atop.py::test_blockwise_diamond_fusion 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_diamond_fusion 
dask/array/tests/test_atop.py::test_blockwise_non_blockwise_output 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_non_blockwise_output 
dask/array/tests/test_atop.py::test_top_len 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_top_len 
dask/array/tests/test_atop.py::test_inner_compute 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_inner_compute 
dask/array/tests/test_atop.py::test_common_token_names_args[_] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[40-5-chunks4-False-True-None] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[40-5-chunks5-True-True-None] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_common_token_names_args[_] 
dask/array/tests/test_atop.py::test_common_token_names_args[_0] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_common_token_names_args[_0] 
dask/array/tests/test_atop.py::test_common_token_names_args[_1] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_common_token_names_args[_1] 
dask/array/tests/test_atop.py::test_common_token_names_args[.] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_common_token_names_args[.] 
dask/array/tests/test_atop.py::test_common_token_names_args[.0] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_common_token_names_args[.0] 
dask/array/tests/test_atop.py::test_common_token_names_kwargs[_0] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_common_token_names_kwargs[_0] 
dask/array/tests/test_atop.py::test_common_token_names_kwargs[_1] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_common_token_names_kwargs[_1] 
dask/array/tests/test_atop.py::test_common_token_names_kwargs[.] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_common_token_names_kwargs[.] 
dask/array/tests/test_atop.py::test_common_token_names_kwargs[.0] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_common_token_names_kwargs[.0] 
dask/array/tests/test_atop.py::test_common_token_names_kwargs[_] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_common_token_names_kwargs[_] 
dask/array/tests/test_atop.py::test_blockwise_names 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_names 
dask/array/tests/test_atop.py::test_blockwise_new_axes 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[40-5-chunks5-True-True-None] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks6-True-False-None] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_new_axes 
dask/array/tests/test_atop.py::test_blockwise_new_axes_2 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_new_axes_2 
dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes[True] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes[True] 
dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes[False] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes[False] 
dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes_front[True] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes_front[True] 
dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes_front[False] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes_front[False] 
dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes_same_dim[True] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes_same_dim[True] 
dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes_same_dim[False] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_stacked_new_axes_same_dim[False] 
dask/array/tests/test_atop.py::test_blockwise_new_axes_chunked 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_new_axes_chunked 
dask/array/tests/test_atop.py::test_blockwise_no_args 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_no_args 
dask/array/tests/test_atop.py::test_blockwise_no_array_args 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_no_array_args 
dask/array/tests/test_atop.py::test_blockwise_kwargs 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_kwargs 
dask/array/tests/test_atop.py::test_blockwise_chunks 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_chunks 
dask/array/tests/test_atop.py::test_blockwise_numpy_arg 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_blockwise_numpy_arg 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks6-True-False-None] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks7-True-False-None] 
dask/array/tests/test_atop.py::test_bag_array_conversion 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_bag_array_conversion 
dask/array/tests/test_atop.py::test_svd 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_svd 
dask/array/tests/test_atop.py::test_args_delayed 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_args_delayed 
dask/array/tests/test_atop.py::test_namedtuple[tup0] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_namedtuple[tup0] 
dask/array/tests/test_atop.py::test_namedtuple[tup1] 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_namedtuple[tup1] 
dask/array/tests/test_atop.py::test_validate_top_inputs 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_validate_top_inputs 
dask/array/tests/test_atop.py::test_dont_merge_before_reductions 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_dont_merge_before_reductions 
dask/array/tests/test_atop.py::test_atop_legacy 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_atop_legacy 
dask/array/tests/test_atop.py::test_non_hlg 
[gw0] [  7%] PASSED dask/array/tests/test_atop.py::test_non_hlg 
dask/array/tests/test_chunk.py::test_keepdims_wrapper_no_axis 
[gw0] [  7%] PASSED dask/array/tests/test_chunk.py::test_keepdims_wrapper_no_axis 
dask/array/tests/test_chunk.py::test_keepdims_wrapper_one_axis 
[gw0] [  7%] PASSED dask/array/tests/test_chunk.py::test_keepdims_wrapper_one_axis 
dask/array/tests/test_chunk.py::test_keepdims_wrapper_two_axes 
[gw0] [  7%] PASSED dask/array/tests/test_chunk.py::test_keepdims_wrapper_two_axes 
dask/array/tests/test_chunk.py::test_coarsen 
[gw0] [  7%] PASSED dask/array/tests/test_chunk.py::test_coarsen 
dask/array/tests/test_chunk.py::test_coarsen_unaligned_shape 
[gw0] [  7%] PASSED dask/array/tests/test_chunk.py::test_coarsen_unaligned_shape 
dask/array/tests/test_chunk.py::test_integer_input 
[gw0] [  7%] PASSED dask/array/tests/test_chunk.py::test_integer_input 
dask/array/tests/test_chunk.py::test_getitem 
[gw0] [  7%] PASSED dask/array/tests/test_chunk.py::test_getitem 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-empty_like-numpy] 
[gw0] [  7%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-empty_like-cupy] 
[gw0] [  7%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-empty-numpy] 
[gw0] [  7%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-empty-cupy] 
[gw0] [  7%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-ones_like-numpy] 
[gw0] [  7%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-ones_like-cupy] 
[gw0] [  7%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-ones-numpy] 
[gw0] [  7%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-ones-cupy] 
[gw0] [  7%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-zeros_like-numpy] 
[gw0] [  7%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-zeros_like-cupy] 
[gw0] [  7%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-zeros-numpy] 
[gw1] [  7%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks7-True-False-None] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks8-True-False-None] 
[gw0] [  7%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-zeros-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-full_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-full_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-full-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-full-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-empty_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-empty_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-empty-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-empty-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-ones_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-ones_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-ones-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-ones-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-zeros_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-zeros_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-zeros-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-zeros-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-full_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-full_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-full-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-full-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-empty_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-empty_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-empty-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-empty-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-ones_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-ones_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-ones-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-ones-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-zeros_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-zeros_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-zeros-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-zeros-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-full_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-full_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-full-numpy] 
[gw1] [  8%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks8-True-False-None] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks9-False-True-None] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-full-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-tuple-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-empty_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-empty_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-empty-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-empty-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-ones_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-ones_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-ones-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-ones-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-zeros_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-zeros_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-zeros-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-zeros-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-full_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-full_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-full-numpy] 
[gw1] [  8%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks9-False-True-None] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-full-numpy] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks10-False-True-None] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-full-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-empty_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-empty_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-empty-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-empty-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-ones_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-ones_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-ones-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-ones-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-zeros_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-zeros_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-zeros-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-zeros-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-full_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-full_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-full-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-full-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-empty_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-empty_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-empty-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-empty-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-ones_like-numpy] 
[gw1] [  8%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks10-False-True-None] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks11-False-True-None] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-ones_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-ones-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-ones-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-zeros_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-zeros_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-zeros-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-zeros-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-full_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-full_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-full-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-full-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-list-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-empty_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-empty_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-empty-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-empty-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-ones_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-ones_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-ones-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-ones-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-zeros_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-zeros_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-zeros-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-zeros-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-full_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-full_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-full-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-full-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-empty_like-numpy] 
[gw1] [  8%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks11-False-True-None] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks12-True-True-None] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-empty_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-empty-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-empty-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-ones_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-ones_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-ones-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-ones-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-zeros_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-zeros_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-zeros-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-zeros-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-full_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-full_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-full-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-full-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-empty_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-empty_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-empty-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-empty-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-ones_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-ones_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-ones-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-ones-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-zeros_like-numpy] 
[gw1] [  8%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks12-True-True-None] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks13-True-True-None] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-zeros_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-zeros-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-zeros-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-full_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-full_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-full-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-full-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-None-shape0-chunks0-asarray-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-empty_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-empty_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-empty-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-empty-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-ones_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-ones_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-ones-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-ones-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-zeros_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-zeros_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-zeros-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-zeros-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-full_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-full_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-full-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-full-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-empty_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-empty_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-empty-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-empty-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-ones_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-ones_like-cupy] 
[gw1] [  8%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks13-True-True-None] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-ones-numpy] 
dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks14-True-True-None] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-ones-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-zeros_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-zeros_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-zeros-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-zeros-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-full_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-full_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-full-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-full-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-empty_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-empty_like-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-empty-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-empty-cupy] 
[gw0] [  8%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-ones_like-numpy] 
[gw0] [  8%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-ones_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-ones-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-ones-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-zeros_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-zeros_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-zeros-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-zeros-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-full_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-full_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-full-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-full-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-tuple-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-empty_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-empty_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-empty-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-empty-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-ones_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-ones_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-ones-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-ones-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-zeros_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-zeros_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-zeros-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-zeros-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-full_like-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_tsqr_uncertain[300-10-chunks14-True-True-None] 
dask/array/tests/test_linalg.py::test_tsqr_zero_height_chunks 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-full_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-full-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-full-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-empty_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-empty_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-empty-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-empty-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-ones_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-ones_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-ones-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_tsqr_zero_height_chunks 
dask/array/tests/test_linalg.py::test_sfqr[20-10-10-ValueError] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[20-10-10-ValueError] 
dask/array/tests/test_linalg.py::test_sfqr[20-10-chunks1-ValueError] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[20-10-chunks1-ValueError] 
dask/array/tests/test_linalg.py::test_sfqr[20-10-chunks2-ValueError] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[20-10-chunks2-ValueError] 
dask/array/tests/test_linalg.py::test_sfqr[40-10-chunks3-ValueError] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[40-10-chunks3-ValueError] 
dask/array/tests/test_linalg.py::test_sfqr[128-2-chunks4-ValueError] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[128-2-chunks4-ValueError] 
dask/array/tests/test_linalg.py::test_sfqr[129-2-chunks5-ValueError] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[129-2-chunks5-ValueError] 
dask/array/tests/test_linalg.py::test_sfqr[130-2-chunks6-ValueError] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-ones-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[130-2-chunks6-ValueError] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-ones-cupy] 
dask/array/tests/test_linalg.py::test_sfqr[131-2-chunks7-ValueError] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[131-2-chunks7-ValueError] 
dask/array/tests/test_linalg.py::test_sfqr[300-10-chunks8-ValueError] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[300-10-chunks8-ValueError] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-ones-cupy] 
dask/array/tests/test_linalg.py::test_sfqr[300-10-chunks9-ValueError] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[300-10-chunks9-ValueError] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-zeros_like-numpy] 
dask/array/tests/test_linalg.py::test_sfqr[300-10-chunks10-ValueError] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[300-10-chunks10-ValueError] 
dask/array/tests/test_linalg.py::test_sfqr[10-5-10-None] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-zeros_like-cupy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[10-5-10-None] 
dask/array/tests/test_linalg.py::test_sfqr[5-10-10-None] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-zeros-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-zeros-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-full_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-full_like-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[5-10-10-None] 
dask/array/tests/test_linalg.py::test_sfqr[10-10-10-None] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-full_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-full-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[10-10-10-None] 
dask/array/tests/test_linalg.py::test_sfqr[10-40-chunks14-None] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[10-40-chunks14-None] 
dask/array/tests/test_linalg.py::test_sfqr[10-40-chunks15-None] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[10-40-chunks15-None] 
dask/array/tests/test_linalg.py::test_sfqr[10-40-chunks16-None] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-full-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-empty_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-empty_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-empty-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-empty-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-ones_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-ones_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-ones-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[10-40-chunks16-None] 
dask/array/tests/test_linalg.py::test_sfqr[20-20-10-ValueError] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_sfqr[20-20-10-ValueError] 
dask/array/tests/test_linalg.py::test_qr[20-10-10-None] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-ones-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-zeros_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-zeros_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-zeros-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-zeros-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-full_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-full_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-full-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-full-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-list-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-empty_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-empty_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-empty-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-empty-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-ones_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-ones_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-ones-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-ones-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-zeros_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-zeros_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-zeros-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-zeros-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-full_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-full_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-full-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_qr[20-10-10-None] 
dask/array/tests/test_linalg.py::test_qr[20-10-chunks1-None] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-full-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-empty_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-empty_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-empty-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-empty-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-ones_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-ones_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-ones-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-ones-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-zeros_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-zeros_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-zeros-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_qr[20-10-chunks1-None] 
dask/array/tests/test_linalg.py::test_qr[20-10-chunks2-None] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-zeros-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-full_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-full_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-full-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-full-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-empty_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-empty_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-empty-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-empty-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-ones_like-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_qr[20-10-chunks2-None] 
dask/array/tests/test_linalg.py::test_qr[40-10-chunks3-None] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-ones_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-ones-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-ones-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-zeros_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-zeros_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-zeros-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-zeros-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-full_like-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_qr[40-10-chunks3-None] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-full_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-full_like-cupy] 
dask/array/tests/test_linalg.py::test_qr[128-2-chunks4-None] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-full-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-full-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-C-my-name-shape0-chunks0-asarray-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-empty_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-empty_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-empty-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-empty-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-ones_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-ones_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-ones-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-ones-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-zeros_like-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_qr[128-2-chunks4-None] 
dask/array/tests/test_linalg.py::test_qr[129-2-chunks5-None] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-zeros_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-zeros-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-zeros-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-full_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-full_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-full-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-full-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-empty_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-empty_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-empty-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_qr[129-2-chunks5-None] 
dask/array/tests/test_linalg.py::test_qr[130-2-chunks6-None] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-empty-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-ones_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-ones_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-ones-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-ones-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-zeros_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-zeros_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-zeros-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-zeros-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-full_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-full_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-full-numpy] 
[gw1] [  9%] PASSED dask/array/tests/test_linalg.py::test_qr[130-2-chunks6-None] 
dask/array/tests/test_linalg.py::test_qr[131-2-chunks7-None] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-full-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-empty_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-empty_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-empty-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-empty-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-ones_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-ones_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-ones-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-ones-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-zeros_like-numpy] 
[gw0] [  9%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-zeros_like-cupy] 
[gw0] [  9%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-zeros-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-zeros-numpy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_qr[131-2-chunks7-None] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-zeros-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-full_like-numpy] 
dask/array/tests/test_linalg.py::test_qr[300-10-chunks8-None] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-full_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-full-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-full-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-tuple-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-empty_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-empty_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-empty-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-empty-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-ones_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-ones_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-ones-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-ones-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-zeros_like-numpy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_qr[300-10-chunks8-None] 
dask/array/tests/test_linalg.py::test_qr[300-10-chunks9-None] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-zeros_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-zeros-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-zeros-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-full_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-full_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-full-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-full-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-empty_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-empty_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-empty-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-empty-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-ones_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-ones_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-ones-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-ones-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-zeros_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-zeros_like-cupy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_qr[300-10-chunks9-None] 
dask/array/tests/test_linalg.py::test_qr[300-10-chunks10-None] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-zeros-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-zeros-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-full_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-full_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-full-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-full-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-empty_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-empty_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-empty-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-empty-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-ones_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-ones_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-ones-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-ones-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-zeros_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-zeros_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-zeros-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-zeros-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-full_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-full_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-full-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-full-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-list-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-empty_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-empty_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-empty-numpy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_qr[300-10-chunks10-None] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-empty-cupy] 
dask/array/tests/test_linalg.py::test_qr[10-5-10-None] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-ones_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-ones_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-ones-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-ones-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-zeros_like-numpy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_qr[10-5-10-None] 
dask/array/tests/test_linalg.py::test_qr[5-10-10-None] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-zeros_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-zeros-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-zeros-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-full_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-full_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-full-numpy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_qr[5-10-10-None] 
dask/array/tests/test_linalg.py::test_qr[10-10-10-None] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-full-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-empty_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-empty_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-empty-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-empty-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-ones_like-numpy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_qr[10-10-10-None] 
dask/array/tests/test_linalg.py::test_qr[10-40-chunks14-None] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-ones_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-ones-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-ones-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-zeros_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-zeros_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-zeros-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-zeros-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-zeros-cupy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_qr[10-40-chunks14-None] 
dask/array/tests/test_linalg.py::test_qr[10-40-chunks15-None] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-full_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-full_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-full-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-full-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-empty_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-empty_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-empty-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-empty-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-ones_like-numpy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_qr[10-40-chunks15-None] 
dask/array/tests/test_linalg.py::test_qr[10-40-chunks16-None] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-ones_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-ones-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-ones-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-zeros_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-zeros_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-zeros-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-zeros-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-full_like-numpy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_qr[10-40-chunks16-None] 
dask/array/tests/test_linalg.py::test_qr[20-20-10-NotImplementedError] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_qr[20-20-10-NotImplementedError] 
dask/array/tests/test_linalg.py::test_linalg_consistent_names 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-full_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-full-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-full-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-None-shape0-chunks0-asarray-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-empty_like-numpy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_linalg_consistent_names 
dask/array/tests/test_linalg.py::test_dask_svd_self_consistent[10-20] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-empty_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-empty-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-empty-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-ones_like-numpy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_dask_svd_self_consistent[10-20] 
dask/array/tests/test_linalg.py::test_dask_svd_self_consistent[15-15] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-ones_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-ones-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-ones-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-zeros_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-zeros_like-cupy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_dask_svd_self_consistent[15-15] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-zeros-numpy] 
dask/array/tests/test_linalg.py::test_dask_svd_self_consistent[20-10] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-zeros-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-full_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-full_like-cupy] 
[gw1] [ 10%] PASSED dask/array/tests/test_linalg.py::test_dask_svd_self_consistent[20-10] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-full-numpy] 
dask/array/tests/test_linalg.py::test_svd_compressed_compute[power] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-full-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-empty_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-empty_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-empty-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-empty-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-ones_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-ones_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-ones-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-ones-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-zeros_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-zeros_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-zeros-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-zeros-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-full_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-full_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-full-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-full-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-empty_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-empty_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-empty-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-empty-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-ones_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-ones_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-ones-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-ones-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-zeros_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-zeros_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-zeros-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-zeros-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-full_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-full_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-full-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-full-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-tuple-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-empty_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-empty_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-empty-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-empty-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-ones_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-ones_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-ones-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-ones-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-zeros_like-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-zeros_like-cupy] 
[gw0] [ 10%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-zeros-numpy] 
[gw0] [ 10%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-zeros-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-full_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-full_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-full-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-full-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-empty_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-empty_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-empty-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-empty-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-ones_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-ones_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-ones-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-ones-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-zeros_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-zeros_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-zeros-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-zeros-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-full_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-full_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-full-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-full-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-empty_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-empty_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-empty-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-empty-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-ones_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-ones_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-ones-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-ones-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-zeros_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-zeros_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-zeros-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-zeros-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-full_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-full_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-full-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-full-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-list-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-empty_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-empty_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-empty-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-empty-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-ones_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-ones_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-ones-numpy] 
[gw1] [ 11%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_compute[power] 
dask/array/tests/test_linalg.py::test_svd_compressed_compute[QR] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-ones-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-zeros_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-zeros_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-zeros-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-zeros-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-full_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-full_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-full-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-full-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-tuple-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-empty_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-empty_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-empty-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-empty-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-ones_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-ones_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-ones-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-ones-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-zeros_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-zeros_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-zeros-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-zeros-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-full_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-full_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-full-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-full-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-list-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-empty_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-empty_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-empty_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-empty_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-empty-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-empty-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-empty-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-empty-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-ones_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-ones_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-ones_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-ones_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-ones-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-ones-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-ones-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-ones-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-zeros_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-zeros_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-zeros_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-zeros_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-zeros-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-zeros-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-zeros-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-zeros-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-full_like-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-full_like-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-full_like-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-full_like-cupy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-full-numpy] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-full-numpy] 
dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-full-cupy] 
[gw0] [ 11%] SKIPPED dask/array/tests/test_creation.py::test_arr_like[i4-F-my-name-shape0-chunks0-asarray-asarray-full-cupy] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape0-chunks0-None-empty_like-kwargs0] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape0-chunks0-None-empty_like-kwargs0] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape0-chunks0-None-ones_like-kwargs1] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape0-chunks0-None-ones_like-kwargs1] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape0-chunks0-None-zeros_like-kwargs2] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape0-chunks0-None-zeros_like-kwargs2] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape0-chunks0-None-full_like-kwargs3] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape0-chunks0-None-full_like-kwargs3] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape1-chunks1-out_shape1-empty_like-kwargs0] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape1-chunks1-out_shape1-empty_like-kwargs0] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape1-chunks1-out_shape1-ones_like-kwargs1] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape1-chunks1-out_shape1-ones_like-kwargs1] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape1-chunks1-out_shape1-zeros_like-kwargs2] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape1-chunks1-out_shape1-zeros_like-kwargs2] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape1-chunks1-out_shape1-full_like-kwargs3] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape1-chunks1-out_shape1-full_like-kwargs3] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape2-4-20-empty_like-kwargs0] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape2-4-20-empty_like-kwargs0] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape2-4-20-ones_like-kwargs1] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape2-4-20-ones_like-kwargs1] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape2-4-20-zeros_like-kwargs2] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape2-4-20-zeros_like-kwargs2] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape2-4-20-full_like-kwargs3] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape2-4-20-full_like-kwargs3] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape3-chunks3-out_shape3-empty_like-kwargs0] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape3-chunks3-out_shape3-empty_like-kwargs0] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape3-chunks3-out_shape3-ones_like-kwargs1] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape3-chunks3-out_shape3-ones_like-kwargs1] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape3-chunks3-out_shape3-zeros_like-kwargs2] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape3-chunks3-out_shape3-zeros_like-kwargs2] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape3-chunks3-out_shape3-full_like-kwargs3] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape3-chunks3-out_shape3-full_like-kwargs3] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape4-None-out_shape4-empty_like-kwargs0] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape4-None-out_shape4-empty_like-kwargs0] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape4-None-out_shape4-ones_like-kwargs1] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape4-None-out_shape4-ones_like-kwargs1] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape4-None-out_shape4-zeros_like-kwargs2] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape4-None-out_shape4-zeros_like-kwargs2] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape4-None-out_shape4-full_like-kwargs3] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape4-None-out_shape4-full_like-kwargs3] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape5-chunks5-out_shape5-empty_like-kwargs0] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape5-chunks5-out_shape5-empty_like-kwargs0] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape5-chunks5-out_shape5-ones_like-kwargs1] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape5-chunks5-out_shape5-ones_like-kwargs1] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape5-chunks5-out_shape5-zeros_like-kwargs2] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape5-chunks5-out_shape5-zeros_like-kwargs2] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape5-chunks5-out_shape5-full_like-kwargs3] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape5-chunks5-out_shape5-full_like-kwargs3] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape6-chunks6-out_shape6-empty_like-kwargs0] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape6-chunks6-out_shape6-empty_like-kwargs0] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape6-chunks6-out_shape6-ones_like-kwargs1] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape6-chunks6-out_shape6-ones_like-kwargs1] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape6-chunks6-out_shape6-zeros_like-kwargs2] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape6-chunks6-out_shape6-zeros_like-kwargs2] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape6-chunks6-out_shape6-full_like-kwargs3] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape6-chunks6-out_shape6-full_like-kwargs3] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape7-auto-out_shape7-empty_like-kwargs0] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape7-auto-out_shape7-empty_like-kwargs0] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape7-auto-out_shape7-ones_like-kwargs1] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape7-auto-out_shape7-ones_like-kwargs1] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape7-auto-out_shape7-zeros_like-kwargs2] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape7-auto-out_shape7-zeros_like-kwargs2] 
dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape7-auto-out_shape7-full_like-kwargs3] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arr_like_shape[i4-shape7-auto-out_shape7-full_like-kwargs3] 
dask/array/tests/test_creation.py::test_linspace[True] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_linspace[True] 
dask/array/tests/test_creation.py::test_linspace[False] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_linspace[False] 
dask/array/tests/test_creation.py::test_arange 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-uint8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-uint64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-int8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-int64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-float32] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-float64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-int] 
[gw1] [ 11%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_compute[QR] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-int] 
dask/array/tests/test_linalg.py::test_svd_compressed[iterator0] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-float] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-uint8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-uint64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-int8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-int64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-float32] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-float64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-int] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-float] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-uint64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-uint8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-uint64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-int8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-int64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-float32] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-float64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-int] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-float] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-uint8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-uint64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-int8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-int64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-float32] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-float64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-int] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-float] 
[gw1] [ 11%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed[iterator0] 
dask/array/tests/test_linalg.py::test_svd_compressed[iterator1] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-uint8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-uint64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-int8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-int64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-float32] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-float64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-int] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-float] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float32-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-uint8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-uint64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-int8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-int64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-float32] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-float64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-int] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-float] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-uint8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-uint64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-int8] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-int64] 
[gw0] [ 11%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-int-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint8-float-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-int8] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed[iterator1] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-int64] 
dask/array/tests/test_linalg.py::test_svd_compressed_dtype_preservation[float32-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-uint8] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_dtype_preservation[float32-float32] 
dask/array/tests/test_linalg.py::test_svd_compressed_dtype_preservation[float64-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-float64] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_dtype_preservation[float64-float64] 
dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float32-chunks0] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-float] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float32-chunks0] 
dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float32-chunks1] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-uint64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-int8] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float32-chunks1] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-int8] 
dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float32-chunks2] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-float64] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float32-chunks2] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-float64] 
dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float64-chunks0] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int8-float] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float64-chunks0] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-uint8] 
dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float64-chunks1] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-int8] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float64-chunks1] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-int64] 
dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float64-chunks2] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-float64] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_dtype_preservation[float64-chunks2] 
dask/array/tests/test_linalg.py::test_svd_compressed_deterministic 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float32-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-int-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[uint64-float-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-uint64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-int] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_deterministic 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-5-5] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float32-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-int8] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-5-5] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-5-10] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-float] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-5-10] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-5-15] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-int-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int8-float-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-float32] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-5-15] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-5-20] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-int8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-int] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-float] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-uint64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-uint8] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-uint64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-int8] 
[gw1] [ 12%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-5-20] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-10-5] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-int64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-float32] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-float64] 
[gw0] [ 12%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-float64] 
[gw1] [ 13%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-10-5] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-10-10] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float32-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-uint64] 
[gw1] [ 13%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-10-10] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-10-15] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-int] 
[gw1] [ 13%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-10-15] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-10-20] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-int-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int64-float-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-int64] 
[gw1] [ 13%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-10-20] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-float32] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-15-5] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-uint64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-float] 
[gw1] [ 13%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-15-5] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-15-10] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-float32] 
[gw1] [ 13%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-15-10] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-float64] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-15-15] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float32-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-float32] 
[gw1] [ 13%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-15-15] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-15-20] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-int-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float32-float-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-float64] 
[gw1] [ 13%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-15-20] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-20-5] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-uint64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-uint64] 
[gw1] [ 13%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-20-5] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-20-10] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-int] 
[gw1] [ 13%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-20-10] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-float] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-20-15] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float32-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-int] 
[gw1] [ 13%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-20-15] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-20-20] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-int64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-float32] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-float64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-int] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-float] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-int-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-uint8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-uint64] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-int8] 
[gw0] [ 13%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-float64] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks0-5-20-20] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-int] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-5-5] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-float] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float64-float-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-float] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-5-5] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-5-10] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-float] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-5-10] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-uint64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-uint8] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-5-15] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-float] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-5-15] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-5-20] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-5-20] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-10-5] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-10-5] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-10-10] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-float] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-float64] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-10-10] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-10-15] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-float] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float32-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-float] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-int] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-10-15] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-float] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-10-20] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-int-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-float] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[int-float-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-float64] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-10-20] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-15-5] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-float] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-float] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-uint64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-int64] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-15-5] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-15-10] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-float] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int8-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-float] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-15-10] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-uint8] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-15-15] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-float] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float32-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-float] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-15-15] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-15-20] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float64-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-int] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-int] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-float] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-int-float] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-int8] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-int64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-float32] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-float64] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-int] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-15-20] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-int] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-20-5] 
dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-float] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_infer[float-float-float] 
dask/array/tests/test_creation.py::test_arange_dtype_force[uint8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_force[uint8] 
dask/array/tests/test_creation.py::test_arange_dtype_force[uint64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_force[uint64] 
dask/array/tests/test_creation.py::test_arange_dtype_force[int8] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_force[int8] 
dask/array/tests/test_creation.py::test_arange_dtype_force[int64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_force[int64] 
dask/array/tests/test_creation.py::test_arange_dtype_force[float32] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_force[float32] 
dask/array/tests/test_creation.py::test_arange_dtype_force[float64] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_dtype_force[float64] 
dask/array/tests/test_creation.py::test_arange_very_large_args[auto-9223372036854765808-9223372036854775807-1] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[auto-9223372036854765808-9223372036854775807-1] 
dask/array/tests/test_creation.py::test_arange_very_large_args[auto-9223372036854775807-9223372036854765808--1] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[auto-9223372036854775807-9223372036854765808--1] 
dask/array/tests/test_creation.py::test_arange_very_large_args[auto-0-9223372036854775807-9223372036854765808] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[auto-0-9223372036854775807-9223372036854765808] 
dask/array/tests/test_creation.py::test_arange_very_large_args[auto-0.0-9223372036854775807-9223372036854765808] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[auto-0.0-9223372036854775807-9223372036854765808] 
dask/array/tests/test_creation.py::test_arange_very_large_args[auto-0.0--9131138316486228481--92233720368547759] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[auto-0.0--9131138316486228481--92233720368547759] 
dask/array/tests/test_creation.py::test_arange_very_large_args[auto--72057594037927945--72057594037927938-1.0] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[auto--72057594037927945--72057594037927938-1.0] 
dask/array/tests/test_creation.py::test_arange_very_large_args[auto--72057594037927945--72057594037927938-1.5] 
[gw0] [ 14%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[auto--72057594037927945--72057594037927938-1.5] 
dask/array/tests/test_creation.py::test_arange_very_large_args[1-9223372036854765808-9223372036854775807-1] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-20-5] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-20-10] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-20-10] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-20-15] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-20-15] 
dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-20-20] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_svd_compressed_shapes[chunks1-5-20-20] 
dask/array/tests/test_linalg.py::test_lu_1 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_lu_1 
dask/array/tests/test_linalg.py::test_lu_2[10] 
[gw1] [ 14%] SKIPPED dask/array/tests/test_linalg.py::test_lu_2[10] 
dask/array/tests/test_linalg.py::test_lu_2[20] 
[gw1] [ 14%] SKIPPED dask/array/tests/test_linalg.py::test_lu_2[20] 
dask/array/tests/test_linalg.py::test_lu_2[30] 
[gw1] [ 14%] SKIPPED dask/array/tests/test_linalg.py::test_lu_2[30] 
dask/array/tests/test_linalg.py::test_lu_2[50] 
[gw1] [ 14%] SKIPPED dask/array/tests/test_linalg.py::test_lu_2[50] 
dask/array/tests/test_linalg.py::test_lu_3[50] 
[gw1] [ 14%] SKIPPED dask/array/tests/test_linalg.py::test_lu_3[50] 
dask/array/tests/test_linalg.py::test_lu_3[100] 
[gw1] [ 14%] SKIPPED dask/array/tests/test_linalg.py::test_lu_3[100] 
dask/array/tests/test_linalg.py::test_lu_3[200] 
[gw1] [ 14%] SKIPPED dask/array/tests/test_linalg.py::test_lu_3[200] 
dask/array/tests/test_linalg.py::test_lu_errors 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_lu_errors 
dask/array/tests/test_linalg.py::test_solve_triangular_vector[20-10] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_solve_triangular_vector[20-10] 
dask/array/tests/test_linalg.py::test_solve_triangular_vector[50-10] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_solve_triangular_vector[50-10] 
dask/array/tests/test_linalg.py::test_solve_triangular_vector[70-20] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_solve_triangular_vector[70-20] 
dask/array/tests/test_linalg.py::test_solve_triangular_matrix[20-10] 
[gw1] [ 14%] PASSED dask/array/tests/test_linalg.py::test_solve_triangular_matrix[20-10] 
dask/array/tests/test_linalg.py::test_solve_triangular_matrix[50-10] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_solve_triangular_matrix[50-10] 
dask/array/tests/test_linalg.py::test_solve_triangular_matrix[50-20] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_solve_triangular_matrix[50-20] 
dask/array/tests/test_linalg.py::test_solve_triangular_matrix2[20-10] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_solve_triangular_matrix2[20-10] 
dask/array/tests/test_linalg.py::test_solve_triangular_matrix2[50-10] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_solve_triangular_matrix2[50-10] 
dask/array/tests/test_linalg.py::test_solve_triangular_matrix2[50-20] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_solve_triangular_matrix2[50-20] 
dask/array/tests/test_linalg.py::test_solve_triangular_errors 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_solve_triangular_errors 
dask/array/tests/test_linalg.py::test_solve[20-10] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_solve[20-10] 
dask/array/tests/test_linalg.py::test_solve[50-10] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[1-9223372036854765808-9223372036854775807-1] 
dask/array/tests/test_creation.py::test_arange_very_large_args[1-9223372036854775807-9223372036854765808--1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_solve[50-10] 
dask/array/tests/test_linalg.py::test_inv[20-10] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_inv[20-10] 
dask/array/tests/test_linalg.py::test_inv[50-10] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_inv[50-10] 
dask/array/tests/test_linalg.py::test_solve_assume_a[20-10] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_solve_assume_a[20-10] 
dask/array/tests/test_linalg.py::test_solve_assume_a[30-6] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[1-9223372036854775807-9223372036854765808--1] 
dask/array/tests/test_creation.py::test_arange_very_large_args[1-0-9223372036854775807-9223372036854765808] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[1-0-9223372036854775807-9223372036854765808] 
dask/array/tests/test_creation.py::test_arange_very_large_args[1-0.0-9223372036854775807-9223372036854765808] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[1-0.0-9223372036854775807-9223372036854765808] 
dask/array/tests/test_creation.py::test_arange_very_large_args[1-0.0--9131138316486228481--92233720368547759] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[1-0.0--9131138316486228481--92233720368547759] 
dask/array/tests/test_creation.py::test_arange_very_large_args[1--72057594037927945--72057594037927938-1.0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[1--72057594037927945--72057594037927938-1.0] 
dask/array/tests/test_creation.py::test_arange_very_large_args[1--72057594037927945--72057594037927938-1.5] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_arange_very_large_args[1--72057594037927945--72057594037927938-1.5] 
dask/array/tests/test_creation.py::test_arange_cast_float_int_step 
[gw0] [ 15%] XFAIL dask/array/tests/test_creation.py::test_arange_cast_float_int_step 
dask/array/tests/test_creation.py::test_arange_float_step 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_arange_float_step 
dask/array/tests/test_creation.py::test_indices_wrong_chunks 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_indices_wrong_chunks 
dask/array/tests/test_creation.py::test_indices_dimensions_chunks 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_indices_dimensions_chunks 
dask/array/tests/test_creation.py::test_empty_indices 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_empty_indices 
dask/array/tests/test_creation.py::test_indices 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_indices 
dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes0-chunks0] 
dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes1-chunks1] 
dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes2-chunks2] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes2-chunks2] 
dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes3-chunks3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes3-chunks3] 
dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes4-chunks4] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_solve_assume_a[30-6] 
dask/array/tests/test_linalg.py::test_cholesky[20-10] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_cholesky[20-10] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes4-chunks4] 
dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes5-chunks5] 
dask/array/tests/test_linalg.py::test_cholesky[12-3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-ij-shapes5-chunks5] 
dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes0-chunks0] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_cholesky[12-3] 
dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes1-chunks1] 
dask/array/tests/test_linalg.py::test_cholesky[30-3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes1-chunks1] 
dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes2-chunks2] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes2-chunks2] 
dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes3-chunks3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes3-chunks3] 
dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes4-chunks4] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes4-chunks4] 
dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes5-chunks5] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[False-xy-shapes5-chunks5] 
dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes0-chunks0] 
dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes1-chunks1] 
dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes2-chunks2] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes2-chunks2] 
dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes3-chunks3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes3-chunks3] 
dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes4-chunks4] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_cholesky[30-3] 
dask/array/tests/test_linalg.py::test_cholesky[30-6] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes4-chunks4] 
dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes5-chunks5] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-ij-shapes5-chunks5] 
dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes0-chunks0] 
dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes1-chunks1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_cholesky[30-6] 
dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes2-chunks2] 
dask/array/tests/test_linalg.py::test_lstsq[20-10-5-False] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes2-chunks2] 
dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes3-chunks3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes3-chunks3] 
dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes4-chunks4] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes4-chunks4] 
dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes5-chunks5] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid[True-xy-shapes5-chunks5] 
dask/array/tests/test_creation.py::test_meshgrid_inputcoercion 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_meshgrid_inputcoercion 
dask/array/tests/test_creation.py::test_tri[3-None-0-float-auto] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tri[3-None-0-float-auto] 
dask/array/tests/test_creation.py::test_tri[4-None-0-float-auto] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tri[4-None-0-float-auto] 
dask/array/tests/test_creation.py::test_tri[3-4-0-bool-auto] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tri[3-4-0-bool-auto] 
dask/array/tests/test_creation.py::test_tri[3-None-1-int-auto] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tri[3-None-1-int-auto] 
dask/array/tests/test_creation.py::test_tri[3-None--1-int-auto] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tri[3-None--1-int-auto] 
dask/array/tests/test_creation.py::test_tri[3-None-2-int-1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tri[3-None-2-int-1] 
dask/array/tests/test_creation.py::test_tri[6-8--2-int-chunks6] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tri[6-8--2-int-chunks6] 
dask/array/tests/test_creation.py::test_tri[6-8-0-int-chunks7] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tri[6-8-0-int-chunks7] 
dask/array/tests/test_creation.py::test_eye 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_lstsq[20-10-5-False] 
dask/array/tests/test_linalg.py::test_lstsq[20-10-5-True] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_eye 
dask/array/tests/test_creation.py::test_diag_bad_input[0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_bad_input[0] 
dask/array/tests/test_creation.py::test_diag_bad_input[3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_bad_input[3] 
dask/array/tests/test_creation.py::test_diag_bad_input[-3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_bad_input[-3] 
dask/array/tests/test_creation.py::test_diag_bad_input[8] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_bad_input[8] 
dask/array/tests/test_creation.py::test_diag_2d_array_creation[0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_2d_array_creation[0] 
dask/array/tests/test_creation.py::test_diag_2d_array_creation[3] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_lstsq[20-10-5-True] 
dask/array/tests/test_linalg.py::test_lstsq[100-10-10-False] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_2d_array_creation[3] 
dask/array/tests/test_creation.py::test_diag_2d_array_creation[-3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_2d_array_creation[-3] 
dask/array/tests/test_creation.py::test_diag_2d_array_creation[8] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_lstsq[100-10-10-False] 
dask/array/tests/test_linalg.py::test_lstsq[100-10-10-True] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_2d_array_creation[8] 
dask/array/tests/test_creation.py::test_diag_extraction[0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_extraction[0] 
dask/array/tests/test_creation.py::test_diag_extraction[3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_extraction[3] 
dask/array/tests/test_creation.py::test_diag_extraction[-3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_extraction[-3] 
dask/array/tests/test_creation.py::test_diag_extraction[8] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diag_extraction[8] 
dask/array/tests/test_creation.py::test_creation_data_producers 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_creation_data_producers 
dask/array/tests/test_creation.py::test_diagonal 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_lstsq[100-10-10-True] 
dask/array/tests/test_linalg.py::test_no_chunks_svd 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_no_chunks_svd 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks0-shape0] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks0-shape0] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks0-shape1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks0-shape1] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks0-shape2] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_diagonal 
dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs0-None] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs0-None] 
dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs0-f8] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs0-f8] 
dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs0-i8] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs0-i8] 
dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs1-None] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks0-shape2] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks1-shape0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs1-None] 
dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs1-f8] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs1-f8] 
dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs1-i8] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs1-i8] 
dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs2-None] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs2-None] 
dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs2-f8] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks1-shape0] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks1-shape1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs2-f8] 
dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs2-i8] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_fromfunction[<lambda>-kwargs2-i8] 
dask/array/tests/test_creation.py::test_repeat 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks1-shape1] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks1-shape2] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks1-shape2] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks2-shape0] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks2-shape0] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks2-shape1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks2-shape1] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks2-shape2] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f4-chunks2-shape2] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks0-shape0] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks0-shape0] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks0-shape1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks0-shape1] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks0-shape2] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks0-shape2] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks1-shape0] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks1-shape0] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks1-shape1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks1-shape1] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks1-shape2] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks1-shape2] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks2-shape0] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks2-shape0] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks2-shape1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks2-shape1] 
dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks2-shape2] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_correction[f8-chunks2-shape2] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[True-f2] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[True-f2] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[True-f4] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[True-f4] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_repeat 
dask/array/tests/test_creation.py::test_tile_basic[2] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[True-f8] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[True-f8] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[True-f16] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[True-f16] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[True-c8] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[True-c8] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[True-c16] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_basic[2] 
dask/array/tests/test_creation.py::test_tile_basic[reps1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[True-c16] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[True-c32] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[True-c32] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[False-f2] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[False-f2] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[False-f4] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[False-f4] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[False-f8] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[False-f8] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_basic[reps1] 
dask/array/tests/test_creation.py::test_tile_basic[reps2] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[False-f16] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[False-f16] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[False-c8] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[False-c8] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[False-c16] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_basic[reps2] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[False-c16] 
dask/array/tests/test_creation.py::test_tile_basic[reps3] 
dask/array/tests/test_linalg.py::test_svd_flip_sign[False-c32] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_flip_sign[False-c32] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_basic[reps3] 
dask/array/tests/test_creation.py::test_tile_basic[reps4] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_basic[reps4] 
dask/array/tests/test_creation.py::test_tile_chunks[0-shape0-chunks0] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape0-chunks0] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape0-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[0-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_chunks[0-shape1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[0-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_chunks[1-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[1-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_chunks[1-shape1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[1-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_chunks[2-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[2-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_chunks[2-shape1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[2-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_chunks[3-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[3-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_chunks[3-shape1-chunks1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape0-chunks1] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape0-chunks2] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[3-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_chunks[5-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[5-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_chunks[5-shape1-chunks1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape0-chunks2] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape0-chunks3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[5-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_chunks[reps5-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[reps5-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_chunks[reps5-shape1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[reps5-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_chunks[reps6-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[reps6-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_chunks[reps6-shape1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_chunks[reps6-shape1-chunks1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape0-chunks3] 
dask/array/tests/test_creation.py::test_tile_neg_reps[-1-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_neg_reps[-1-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_neg_reps[-1-shape1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_neg_reps[-1-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_neg_reps[-5-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_neg_reps[-5-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_neg_reps[-5-shape1-chunks1] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape1-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_neg_reps[-5-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_zero_reps[0-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_zero_reps[0-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_zero_reps[0-shape1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_zero_reps[0-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_zero_reps[reps1-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_zero_reps[reps1-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_zero_reps[reps1-shape1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_zero_reps[reps1-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_zero_reps[reps2-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_zero_reps[reps2-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_zero_reps[reps2-shape1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_zero_reps[reps2-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_zero_reps[reps3-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_zero_reps[reps3-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_zero_reps[reps3-shape1-chunks1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape1-chunks0] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_zero_reps[reps3-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_empty_array[2-shape0-chunks0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_empty_array[2-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_empty_array[2-shape1-chunks1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_empty_array[2-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_empty_array[reps1-shape0-chunks0] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape1-chunks1] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape1-chunks2] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_empty_array[reps1-shape0-chunks0] 
dask/array/tests/test_creation.py::test_tile_empty_array[reps1-shape1-chunks1] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape1-chunks2] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_empty_array[reps1-shape1-chunks1] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape0] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape1-chunks3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape0] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape1] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape2] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape2] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape3] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape4] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape4] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape5] 
[gw1] [ 15%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape1-chunks3] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps0-shape5] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape2-chunks0] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape0] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape0] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape1] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape1] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape2] 
[gw0] [ 15%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape2] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape3] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape3] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape4] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape2-chunks0] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape2-chunks1] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape4] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape5] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps1-shape5] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape0] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape1] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape1] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape2-chunks1] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape2] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape2-chunks2] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape2] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape3] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape3] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape4] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape4] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape5] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps2-shape5] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape0] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape2-chunks2] 
dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape2-chunks3] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape0] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape1] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape1] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape2] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape2] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape3] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape3] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape4] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape4] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape5] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_svd_supported_array_shapes[shape2-chunks3] 
dask/array/tests/test_linalg.py::test_svd_incompatible_chunking 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps3-shape5] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape0] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_svd_incompatible_chunking 
dask/array/tests/test_linalg.py::test_svd_incompatible_dimensions[0] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_svd_incompatible_dimensions[0] 
dask/array/tests/test_linalg.py::test_svd_incompatible_dimensions[1] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_svd_incompatible_dimensions[1] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape0] 
dask/array/tests/test_linalg.py::test_svd_incompatible_dimensions[3] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape1] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_svd_incompatible_dimensions[3] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-None-shape0-chunks0-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape1] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape2] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-None-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-None-shape1-chunks1-0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape2] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape3] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape3] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-None-shape1-chunks1-0] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape4] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-None-shape2-chunks2-axis2] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape4] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape5] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-None-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-None-shape3-chunks3-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps4-shape5] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape0] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape1] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-None-shape3-chunks3-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-1-shape0-chunks0-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape1] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape2] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape2] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-1-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-1-shape1-chunks1-0] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape3] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape3] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape4] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-1-shape1-chunks1-0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape4] 
dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape5] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-1-shape2-chunks2-axis2] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_tile_np_kroncompare_examples[reps5-shape5] 
dask/array/tests/test_creation.py::test_pad_0_width[shape0-chunks0-0-constant-kwargs0] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-1-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-1-shape3-chunks3-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_0_width[shape0-chunks0-0-constant-kwargs0] 
dask/array/tests/test_creation.py::test_pad_0_width[shape1-chunks1-0-edge-kwargs1] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_0_width[shape1-chunks1-0-edge-kwargs1] 
dask/array/tests/test_creation.py::test_pad_0_width[shape2-chunks2-0-linear_ramp-kwargs2] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-1-shape3-chunks3-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_0_width[shape2-chunks2-0-linear_ramp-kwargs2] 
dask/array/tests/test_creation.py::test_pad_0_width[shape3-chunks3-0-reflect-kwargs3] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False--1-shape0-chunks0-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_0_width[shape3-chunks3-0-reflect-kwargs3] 
dask/array/tests/test_creation.py::test_pad_0_width[shape4-chunks4-0-symmetric-kwargs4] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False--1-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False--1-shape1-chunks1-0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_0_width[shape4-chunks4-0-symmetric-kwargs4] 
dask/array/tests/test_creation.py::test_pad_0_width[shape5-chunks5-0-wrap-kwargs5] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False--1-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False--1-shape2-chunks2-axis2] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_0_width[shape5-chunks5-0-wrap-kwargs5] 
dask/array/tests/test_creation.py::test_pad_0_width[shape6-chunks6-0-empty-kwargs6] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False--1-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False--1-shape3-chunks3-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_0_width[shape6-chunks6-0-empty-kwargs6] 
dask/array/tests/test_creation.py::test_pad[shape0-chunks0-1-constant-kwargs0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape0-chunks0-1-constant-kwargs0] 
dask/array/tests/test_creation.py::test_pad[shape1-chunks1-2-constant-kwargs1] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape1-chunks1-2-constant-kwargs1] 
dask/array/tests/test_creation.py::test_pad[shape2-chunks2-2-constant-kwargs2] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False--1-shape3-chunks3-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-inf-shape0-chunks0-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape2-chunks2-2-constant-kwargs2] 
dask/array/tests/test_creation.py::test_pad[shape3-chunks3-pad_width3-constant-kwargs3] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-inf-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-inf-shape1-chunks1-0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape3-chunks3-pad_width3-constant-kwargs3] 
dask/array/tests/test_creation.py::test_pad[shape4-chunks4-pad_width4-constant-kwargs4] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-inf-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-inf-shape2-chunks2-axis2] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape4-chunks4-pad_width4-constant-kwargs4] 
dask/array/tests/test_creation.py::test_pad[shape5-chunks5-3-edge-kwargs5] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-inf-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False-inf-shape3-chunks3-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape5-chunks5-3-edge-kwargs5] 
dask/array/tests/test_creation.py::test_pad[shape6-chunks6-3-linear_ramp-kwargs6] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape6-chunks6-3-linear_ramp-kwargs6] 
dask/array/tests/test_creation.py::test_pad[shape7-chunks7-3-linear_ramp-kwargs7] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False-inf-shape3-chunks3-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False--inf-shape0-chunks0-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape7-chunks7-3-linear_ramp-kwargs7] 
dask/array/tests/test_creation.py::test_pad[shape8-chunks8-pad_width8-linear_ramp-kwargs8] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False--inf-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False--inf-shape1-chunks1-0] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False--inf-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False--inf-shape2-chunks2-axis2] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape8-chunks8-pad_width8-linear_ramp-kwargs8] 
dask/array/tests/test_creation.py::test_pad[shape9-chunks9-pad_width9-reflect-kwargs9] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False--inf-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[False--inf-shape3-chunks3-None] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[False--inf-shape3-chunks3-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-None-shape0-chunks0-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape9-chunks9-pad_width9-reflect-kwargs9] 
dask/array/tests/test_creation.py::test_pad[shape10-chunks10-pad_width10-symmetric-kwargs10] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-None-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-None-shape1-chunks1-0] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-None-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-None-shape2-chunks2-axis2] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape10-chunks10-pad_width10-symmetric-kwargs10] 
dask/array/tests/test_creation.py::test_pad[shape11-chunks11-pad_width11-wrap-kwargs11] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-None-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-None-shape3-chunks3-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape11-chunks11-pad_width11-wrap-kwargs11] 
dask/array/tests/test_creation.py::test_pad[shape12-chunks12-pad_width12-maximum-kwargs12] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape12-chunks12-pad_width12-maximum-kwargs12] 
dask/array/tests/test_creation.py::test_pad[shape13-chunks13-pad_width13-mean-kwargs13] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-None-shape3-chunks3-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-1-shape0-chunks0-None] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-1-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-1-shape1-chunks1-0] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-1-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-1-shape2-chunks2-axis2] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape13-chunks13-pad_width13-mean-kwargs13] 
dask/array/tests/test_creation.py::test_pad[shape14-chunks14-pad_width14-minimum-kwargs14] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-1-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-1-shape3-chunks3-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape14-chunks14-pad_width14-minimum-kwargs14] 
dask/array/tests/test_creation.py::test_pad[shape15-chunks15-1-empty-kwargs15] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-1-shape3-chunks3-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True--1-shape0-chunks0-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad[shape15-chunks15-1-empty-kwargs15] 
dask/array/tests/test_creation.py::test_pad_constant_values[np_a0-pad_value0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_constant_values[np_a0-pad_value0] 
dask/array/tests/test_creation.py::test_pad_constant_values[np_a1-0.0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_constant_values[np_a1-0.0] 
dask/array/tests/test_creation.py::test_pad_constant_values[np_a2-pad_value2] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True--1-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True--1-shape1-chunks1-0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_constant_values[np_a2-pad_value2] 
dask/array/tests/test_creation.py::test_pad_constant_values[np_a3-pad_value3] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_constant_values[np_a3-pad_value3] 
dask/array/tests/test_creation.py::test_pad_constant_values[np_a4-00] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True--1-shape1-chunks1-0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_constant_values[np_a4-00] 
dask/array/tests/test_creation.py::test_pad_constant_values[np_a5-pad_value5] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True--1-shape2-chunks2-axis2] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_constant_values[np_a5-pad_value5] 
dask/array/tests/test_creation.py::test_pad_constant_values[np_a6-pad_value6] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True--1-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True--1-shape3-chunks3-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_constant_values[np_a6-pad_value6] 
dask/array/tests/test_creation.py::test_pad_constant_values[np_a7-pad_value7] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_constant_values[np_a7-pad_value7] 
dask/array/tests/test_creation.py::test_pad_constant_values[np_a8-pad_value8] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_constant_values[np_a8-pad_value8] 
dask/array/tests/test_creation.py::test_pad_constant_values[np_a9-pad_value9] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_constant_values[np_a9-pad_value9] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-2-uint8] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True--1-shape3-chunks3-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-inf-shape0-chunks0-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-2-int16] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-inf-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-inf-shape1-chunks1-0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-2-float32] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-inf-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-inf-shape2-chunks2-axis2] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-inf-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True-inf-shape3-chunks3-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-2-bool] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths1-uint8] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths1-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths1-int16] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths1-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths1-float32] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths1-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths1-bool] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths1-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths2-uint8] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths2-int16] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths2-float32] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths2-bool] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True-inf-shape3-chunks3-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True--inf-shape0-chunks0-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths3-uint8] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True--inf-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True--inf-shape1-chunks1-0] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True--inf-shape1-chunks1-0] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths3-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths3-int16] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True--inf-shape2-chunks2-axis2] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True--inf-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_any_ndim[True--inf-shape3-chunks3-None] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths3-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths3-float32] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_ndim[True--inf-shape3-chunks3-None] 
dask/array/tests/test_linalg.py::test_norm_any_prec[None-False-True-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[None-False-True-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[None-False-True-double] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths3-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths3-bool] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[None-False-True-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[None-False-False-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[None-False-False-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[None-False-False-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[None-False-False-double] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths3-bool] 
dask/array/tests/test_linalg.py::test_norm_any_prec[None-True-True-single] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths4-uint8] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[None-True-True-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[None-True-True-double] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths4-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths4-int16] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[None-True-True-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[None-True-False-single] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths4-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths4-float32] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[None-True-False-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[None-True-False-double] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths4-float32] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[None-True-False-double] 
dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths4-bool] 
dask/array/tests/test_linalg.py::test_norm_any_prec[1-False-True-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[1-False-True-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[1-False-True-double] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[constant-pad_widths4-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-2-uint8] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[1-False-True-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[1-False-False-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[1-False-False-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[1-False-False-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[1-False-False-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[1-True-True-single] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-2-int16] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[1-True-True-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[1-True-True-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[1-True-True-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[1-True-False-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[1-True-False-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[1-True-False-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[1-True-False-double] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-2-float32] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-1-False-True-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-1-False-True-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-1-False-True-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-1-False-True-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-1-False-False-single] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-2-bool] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-1-False-False-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-1-False-False-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-1-False-False-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-1-True-True-single] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths1-uint8] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-1-True-True-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-1-True-True-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-1-True-True-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-1-True-False-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-1-True-False-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-1-True-False-double] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths1-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths1-int16] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-1-True-False-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[inf-False-True-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[inf-False-True-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[inf-False-True-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[inf-False-True-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[inf-False-False-single] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths1-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths1-float32] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[inf-False-False-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[inf-False-False-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[inf-False-False-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[inf-True-True-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[inf-True-True-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[inf-True-True-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[inf-True-True-double] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths1-float32] 
dask/array/tests/test_linalg.py::test_norm_any_prec[inf-True-False-single] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths1-bool] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[inf-True-False-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[inf-True-False-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[inf-True-False-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-False-True-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-False-True-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-False-True-double] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths1-bool] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-False-True-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-False-False-single] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths2-uint8] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-False-False-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-False-False-double] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-False-False-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-True-True-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-True-True-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-True-True-double] 
[gw0] [ 16%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths2-int16] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-True-True-double] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-True-False-single] 
[gw1] [ 16%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-True-False-single] 
dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-True-False-double] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_any_prec[-inf-True-False-double] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-None-shape0-chunks0] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-None-shape0-chunks0] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-None-shape1-chunks1] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-None-shape1-chunks1] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-None-shape2-chunks2] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-None-shape2-chunks2] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-None-shape3-chunks3] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-None-shape3-chunks3] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-None-shape4-chunks4] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-None-shape4-chunks4] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-1-shape0-chunks0] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-1-shape0-chunks0] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-1-shape1-chunks1] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-1-shape1-chunks1] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-1-shape2-chunks2] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths2-float32] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-1-shape2-chunks2] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-1-shape3-chunks3] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-1-shape3-chunks3] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-1-shape4-chunks4] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-1-shape4-chunks4] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False--1-shape0-chunks0] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False--1-shape0-chunks0] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False--1-shape1-chunks1] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False--1-shape1-chunks1] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False--1-shape2-chunks2] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False--1-shape2-chunks2] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False--1-shape3-chunks3] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False--1-shape3-chunks3] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False--1-shape4-chunks4] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False--1-shape4-chunks4] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-inf-shape0-chunks0] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-inf-shape0-chunks0] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-inf-shape1-chunks1] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-inf-shape1-chunks1] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-inf-shape2-chunks2] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-inf-shape2-chunks2] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-inf-shape3-chunks3] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-inf-shape3-chunks3] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths2-float32] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False-inf-shape4-chunks4] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False-inf-shape4-chunks4] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths2-bool] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False--inf-shape0-chunks0] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False--inf-shape0-chunks0] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False--inf-shape1-chunks1] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False--inf-shape1-chunks1] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False--inf-shape2-chunks2] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False--inf-shape2-chunks2] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False--inf-shape3-chunks3] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False--inf-shape3-chunks3] 
dask/array/tests/test_linalg.py::test_norm_any_slice[False--inf-shape4-chunks4] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[False--inf-shape4-chunks4] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-None-shape0-chunks0] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-None-shape0-chunks0] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-None-shape1-chunks1] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-None-shape1-chunks1] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-None-shape2-chunks2] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-None-shape2-chunks2] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-None-shape3-chunks3] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-None-shape3-chunks3] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-None-shape4-chunks4] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-None-shape4-chunks4] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-1-shape0-chunks0] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-1-shape0-chunks0] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-1-shape1-chunks1] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-1-shape1-chunks1] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-1-shape2-chunks2] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-1-shape2-chunks2] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-1-shape3-chunks3] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-1-shape3-chunks3] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-1-shape4-chunks4] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-1-shape4-chunks4] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths2-bool] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True--1-shape0-chunks0] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True--1-shape0-chunks0] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True--1-shape1-chunks1] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True--1-shape1-chunks1] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True--1-shape2-chunks2] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths3-uint8] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True--1-shape2-chunks2] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True--1-shape3-chunks3] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True--1-shape3-chunks3] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True--1-shape4-chunks4] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True--1-shape4-chunks4] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-inf-shape0-chunks0] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-inf-shape0-chunks0] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-inf-shape1-chunks1] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-inf-shape1-chunks1] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-inf-shape2-chunks2] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-inf-shape2-chunks2] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-inf-shape3-chunks3] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-inf-shape3-chunks3] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True-inf-shape4-chunks4] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True-inf-shape4-chunks4] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True--inf-shape0-chunks0] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True--inf-shape0-chunks0] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True--inf-shape1-chunks1] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True--inf-shape1-chunks1] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True--inf-shape2-chunks2] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True--inf-shape2-chunks2] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True--inf-shape3-chunks3] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True--inf-shape3-chunks3] 
dask/array/tests/test_linalg.py::test_norm_any_slice[True--inf-shape4-chunks4] 
[gw1] [ 17%] SKIPPED dask/array/tests/test_linalg.py::test_norm_any_slice[True--inf-shape4-chunks4] 
dask/array/tests/test_linalg.py::test_norm_1dim[False-0-shape0-chunks0-None] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths3-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths3-int16] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False-0-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_1dim[False-0-shape1-chunks1-0] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False-0-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_1dim[False-0-shape2-chunks2-axis2] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths3-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths3-float32] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False-0-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_1dim[False-2-shape0-chunks0-None] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False-2-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_1dim[False-2-shape1-chunks1-0] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths3-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths3-bool] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False-2-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_1dim[False-2-shape2-chunks2-axis2] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False-2-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_1dim[False--2-shape0-chunks0-None] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths3-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths4-uint8] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False--2-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_1dim[False--2-shape1-chunks1-0] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths4-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths4-int16] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False--2-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_1dim[False--2-shape2-chunks2-axis2] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths4-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths4-float32] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False--2-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_1dim[False-0.5-shape0-chunks0-None] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths4-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths4-bool] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False-0.5-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_1dim[False-0.5-shape1-chunks1-0] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[edge-pad_widths4-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-2-uint8] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False-0.5-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_1dim[False-0.5-shape2-chunks2-axis2] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[False-0.5-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_1dim[True-0-shape0-chunks0-None] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True-0-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_1dim[True-0-shape1-chunks1-0] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-2-int16] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True-0-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_1dim[True-0-shape2-chunks2-axis2] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True-0-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_1dim[True-2-shape0-chunks0-None] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-2-int16] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True-2-shape0-chunks0-None] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-2-float32] 
dask/array/tests/test_linalg.py::test_norm_1dim[True-2-shape1-chunks1-0] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True-2-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_1dim[True-2-shape2-chunks2-axis2] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True-2-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_1dim[True--2-shape0-chunks0-None] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-2-bool] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True--2-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_1dim[True--2-shape1-chunks1-0] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True--2-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_1dim[True--2-shape2-chunks2-axis2] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True--2-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_1dim[True-0.5-shape0-chunks0-None] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths1-uint8] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True-0.5-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_1dim[True-0.5-shape1-chunks1-0] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True-0.5-shape1-chunks1-0] 
dask/array/tests/test_linalg.py::test_norm_1dim[True-0.5-shape2-chunks2-axis2] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths1-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths1-int16] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_1dim[True-0.5-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_2dim[False-fro-shape0-chunks0-None] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False-fro-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_2dim[False-fro-shape1-chunks1-axis1] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths1-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths1-float32] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False-fro-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_2dim[False-fro-shape2-chunks2-axis2] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False-fro-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_2dim[False-nuc-shape0-chunks0-None] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths1-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths1-bool] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False-nuc-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_2dim[False-nuc-shape1-chunks1-axis1] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False-nuc-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_2dim[False-nuc-shape2-chunks2-axis2] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths1-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths2-uint8] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False-nuc-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_2dim[False-2-shape0-chunks0-None] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False-2-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_2dim[False-2-shape1-chunks1-axis1] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths2-int16] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False-2-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_2dim[False-2-shape2-chunks2-axis2] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths2-float32] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False-2-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_2dim[False--2-shape0-chunks0-None] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False--2-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_2dim[False--2-shape1-chunks1-axis1] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False--2-shape1-chunks1-axis1] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths2-float32] 
dask/array/tests/test_linalg.py::test_norm_2dim[False--2-shape2-chunks2-axis2] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths2-bool] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[False--2-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_2dim[True-fro-shape0-chunks0-None] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True-fro-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_2dim[True-fro-shape1-chunks1-axis1] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True-fro-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_2dim[True-fro-shape2-chunks2-axis2] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True-fro-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_2dim[True-nuc-shape0-chunks0-None] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True-nuc-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_2dim[True-nuc-shape1-chunks1-axis1] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True-nuc-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_2dim[True-nuc-shape2-chunks2-axis2] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True-nuc-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_2dim[True-2-shape0-chunks0-None] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True-2-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_2dim[True-2-shape1-chunks1-axis1] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths3-uint8] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True-2-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_2dim[True-2-shape2-chunks2-axis2] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True-2-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_2dim[True--2-shape0-chunks0-None] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths3-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths3-int16] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True--2-shape0-chunks0-None] 
dask/array/tests/test_linalg.py::test_norm_2dim[True--2-shape1-chunks1-axis1] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths3-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths3-float32] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True--2-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_2dim[True--2-shape2-chunks2-axis2] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths3-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths3-bool] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_2dim[True--2-shape2-chunks2-axis2] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[False-nuc-shape0-chunks0-axis0] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[False-nuc-shape0-chunks0-axis0] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[False-nuc-shape1-chunks1-axis1] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[False-nuc-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[False-2-shape0-chunks0-axis0] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[False-2-shape0-chunks0-axis0] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[False-2-shape1-chunks1-axis1] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[False-2-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[False--2-shape0-chunks0-axis0] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[False--2-shape0-chunks0-axis0] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[False--2-shape1-chunks1-axis1] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[False--2-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[True-nuc-shape0-chunks0-axis0] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[True-nuc-shape0-chunks0-axis0] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[True-nuc-shape1-chunks1-axis1] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[True-nuc-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[True-2-shape0-chunks0-axis0] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[True-2-shape0-chunks0-axis0] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[True-2-shape1-chunks1-axis1] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths3-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths4-uint8] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[True-2-shape1-chunks1-axis1] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[True--2-shape0-chunks0-axis0] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[True--2-shape0-chunks0-axis0] 
dask/array/tests/test_linalg.py::test_norm_implemented_errors[True--2-shape1-chunks1-axis1] 
[gw1] [ 17%] PASSED dask/array/tests/test_linalg.py::test_norm_implemented_errors[True--2-shape1-chunks1-axis1] 
dask/array/tests/test_map_blocks.py::test_map_blocks_block_id_fusion 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths4-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths4-int16] 
[gw1] [ 17%] PASSED dask/array/tests/test_map_blocks.py::test_map_blocks_block_id_fusion 
dask/array/tests/test_masked.py::test_tokenize_masked_array 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_tokenize_masked_array 
dask/array/tests/test_masked.py::test_from_array_masked_array 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_from_array_masked_array 
dask/array/tests/test_masked.py::test_copy_deepcopy 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths4-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths4-float32] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_copy_deepcopy 
dask/array/tests/test_masked.py::test_basic[<lambda>0] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths4-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths4-bool] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[linear_ramp-pad_widths4-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-2-uint8] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>0] 
dask/array/tests/test_masked.py::test_basic[<lambda>1] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-2-int16] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>1] 
dask/array/tests/test_masked.py::test_basic[<lambda>2] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>2] 
dask/array/tests/test_masked.py::test_basic[<lambda>3] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-2-float32] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>3] 
dask/array/tests/test_masked.py::test_basic[<lambda>4] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-2-bool] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>4] 
dask/array/tests/test_masked.py::test_basic[<lambda>5] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths1-uint8] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>5] 
dask/array/tests/test_masked.py::test_basic[<lambda>6] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>6] 
dask/array/tests/test_masked.py::test_basic[<lambda>7] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths1-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths1-int16] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>7] 
dask/array/tests/test_masked.py::test_basic[<lambda>8] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>8] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths1-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths1-float32] 
dask/array/tests/test_masked.py::test_basic[<lambda>9] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>9] 
dask/array/tests/test_masked.py::test_basic[<lambda>10] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths1-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths1-bool] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>10] 
dask/array/tests/test_masked.py::test_basic[<lambda>11] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>11] 
dask/array/tests/test_masked.py::test_basic[<lambda>12] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths1-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths2-uint8] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>12] 
dask/array/tests/test_masked.py::test_basic[<lambda>13] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths2-int16] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>13] 
dask/array/tests/test_masked.py::test_basic[<lambda>14] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths2-float32] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>14] 
dask/array/tests/test_masked.py::test_basic[<lambda>15] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths2-bool] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>15] 
dask/array/tests/test_masked.py::test_basic[<lambda>16] 
[gw1] [ 17%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>16] 
dask/array/tests/test_masked.py::test_basic[<lambda>17] 
[gw0] [ 17%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-uint8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>17] 
dask/array/tests/test_masked.py::test_basic[<lambda>18] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-int16] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>18] 
dask/array/tests/test_masked.py::test_basic[<lambda>19] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>19] 
dask/array/tests/test_masked.py::test_basic[<lambda>20] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-float32] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>20] 
dask/array/tests/test_masked.py::test_basic[<lambda>21] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>21] 
dask/array/tests/test_masked.py::test_basic[<lambda>22] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-bool] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>22] 
dask/array/tests/test_masked.py::test_basic[<lambda>23] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths3-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-uint8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>23] 
dask/array/tests/test_masked.py::test_basic[<lambda>24] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-int16] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>24] 
dask/array/tests/test_masked.py::test_basic[<lambda>25] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-float32] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>25] 
dask/array/tests/test_masked.py::test_basic[<lambda>26] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-bool] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_basic[<lambda>26] 
dask/array/tests/test_masked.py::test_tensordot 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[maximum-pad_widths4-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-2-uint8] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-2-int16] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-2-float32] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_tensordot 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>0] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>0] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>1] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-2-bool] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>1] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>2] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths1-uint8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>2] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>3] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>3] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>4] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths1-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths1-int16] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>4] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>5] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>5] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>6] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths1-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths1-float32] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>6] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>7] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>7] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>8] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths1-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths1-bool] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>8] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>9] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>9] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>10] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths1-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths2-uint8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>10] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>11] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>11] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>12] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>12] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>13] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths2-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths2-int16] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>13] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>14] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>14] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>15] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths2-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths2-float32] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>15] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>16] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths2-float32] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths2-bool] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>16] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>17] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>17] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>18] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths2-bool] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths3-uint8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>18] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>19] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>19] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>20] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths3-uint8] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths3-int16] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>20] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>21] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>21] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>22] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>22] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>23] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths3-int16] 
dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths3-float32] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>23] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>24] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>24] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>25] 
[gw0] [ 18%] PASSED dask/array/tests/test_creation.py::test_pad_3d_data[mean-pad_widths3-float32] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-index] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>25] 
dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>26] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_concatenate[<lambda>26] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>0] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>0] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>1] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>1] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>2] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-0] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>2] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>3] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>3] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>4] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>4] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>5] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>5] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>6] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>6] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>7] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-columns] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>7] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>8] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>9] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>9] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>10] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>10] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>11] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>11] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>12] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-1] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>12] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>13] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>13] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>14] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>14] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>15] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-index] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>15] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>16] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>16] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>17] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-0] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>17] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>18] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>18] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>19] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>19] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>20] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>20] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>21] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-columns] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>21] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>22] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>22] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>23] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>23] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>24] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>24] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>25] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>25] 
dask/array/tests/test_masked.py::test_mixed_random[<lambda>26] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-1] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_random[<lambda>26] 
dask/array/tests/test_masked.py::test_mixed_output_type 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_mixed_output_type 
dask/array/tests/test_masked.py::test_creation_functions 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_creation_functions 
dask/array/tests/test_masked.py::test_filled 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_filled 
dask/array/tests/test_masked.py::test_reductions[sum-i8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-index] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[sum-i8] 
dask/array/tests/test_masked.py::test_reductions[sum-f8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-0] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[sum-f8] 
dask/array/tests/test_masked.py::test_reductions[prod-i8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-columns] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[prod-i8] 
dask/array/tests/test_masked.py::test_reductions[prod-f8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[prod-f8] 
dask/array/tests/test_masked.py::test_reductions[mean-i8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-1] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[mean-i8] 
dask/array/tests/test_masked.py::test_reductions[mean-f8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-index] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[mean-f8] 
dask/array/tests/test_masked.py::test_reductions[var-i8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-0] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[var-i8] 
dask/array/tests/test_masked.py::test_reductions[var-f8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-columns] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-1] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[var-f8] 
dask/array/tests/test_masked.py::test_reductions[std-i8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-index] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[std-i8] 
dask/array/tests/test_masked.py::test_reductions[std-f8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-0] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-columns] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[std-f8] 
dask/array/tests/test_masked.py::test_reductions[min-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[min-i8] 
dask/array/tests/test_masked.py::test_reductions[min-f8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-1] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[min-f8] 
dask/array/tests/test_masked.py::test_reductions[max-i8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-index] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[max-i8] 
dask/array/tests/test_masked.py::test_reductions[max-f8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-0] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[max-f8] 
dask/array/tests/test_masked.py::test_reductions[any-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[any-i8] 
dask/array/tests/test_masked.py::test_reductions[any-f8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-columns] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[any-f8] 
dask/array/tests/test_masked.py::test_reductions[all-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[all-i8] 
dask/array/tests/test_masked.py::test_reductions[all-f8] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-1] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions[all-f8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[sum-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[sum-i8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[sum-f8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[sum-f8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[prod-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[prod-i8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[prod-f8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[prod-f8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[mean-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[mean-i8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[mean-f8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[mean-f8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[var-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[var-i8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[var-f8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[var-f8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[std-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[std-i8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[std-f8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[std-f8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[min-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[min-i8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[min-f8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[min-f8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[max-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[max-i8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[max-f8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[max-f8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[any-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[any-i8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[any-f8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[any-f8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[all-i8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[all-i8] 
dask/array/tests/test_masked.py::test_reductions_allmasked[all-f8] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_reductions_allmasked[all-f8] 
dask/array/tests/test_masked.py::test_arg_reductions[argmin] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-index] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_arg_reductions[argmin] 
dask/array/tests/test_masked.py::test_arg_reductions[argmax] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-0] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-columns] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_arg_reductions[argmax] 
dask/array/tests/test_masked.py::test_cumulative 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-1] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-index] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-0] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-columns] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-1] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_cumulative 
dask/array/tests/test_masked.py::test_accessors 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_accessors 
dask/array/tests/test_masked.py::test_masked_array 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-index] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_masked_array 
dask/array/tests/test_masked.py::test_set_fill_value 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_set_fill_value 
dask/array/tests/test_masked.py::test_average_weights_with_masked_array[False] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_average_weights_with_masked_array[False] 
dask/array/tests/test_masked.py::test_average_weights_with_masked_array[True] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_average_weights_with_masked_array[True] 
dask/array/tests/test_masked.py::test_arithmetic_results_in_masked 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_arithmetic_results_in_masked 
dask/array/tests/test_masked.py::test_count 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-0] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_count 
dask/array/tests/test_masked.py::test_like_funcs[ones_like] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_like_funcs[ones_like] 
dask/array/tests/test_masked.py::test_like_funcs[zeros_like] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_like_funcs[zeros_like] 
dask/array/tests/test_masked.py::test_like_funcs[empty_like] 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_like_funcs[empty_like] 
dask/array/tests/test_masked.py::test_nonzero 
[gw1] [ 18%] PASSED dask/array/tests/test_masked.py::test_nonzero 
dask/array/tests/test_masked.py::test_where 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-columns] 
[gw0] [ 18%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-1] 
[gw1] [ 19%] PASSED dask/array/tests/test_masked.py::test_where 
dask/array/tests/test_numpy_compat.py::test_basic 
[gw1] [ 19%] PASSED dask/array/tests/test_numpy_compat.py::test_basic 
dask/array/tests/test_numpy_compat.py::test_min_max_round_funcs 
[gw1] [ 19%] PASSED dask/array/tests/test_numpy_compat.py::test_min_max_round_funcs 
dask/array/tests/test_optimization.py::test_optimize_with_getitem_fusion 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_optimize_with_getitem_fusion 
dask/array/tests/test_optimization.py::test_fuse_slice 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_fuse_slice 
dask/array/tests/test_optimization.py::test_fuse_slice_with_lists 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_fuse_slice_with_lists 
dask/array/tests/test_optimization.py::test_nonfusible_fancy_indexing 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_nonfusible_fancy_indexing 
dask/array/tests/test_optimization.py::test_dont_fuse_numpy_arrays 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_dont_fuse_numpy_arrays 
dask/array/tests/test_optimization.py::test_fuse_slices_with_alias 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_fuse_slices_with_alias 
dask/array/tests/test_optimization.py::test_fuse_getter_with_asarray[10] 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_fuse_getter_with_asarray[10] 
dask/array/tests/test_optimization.py::test_fuse_getter_with_asarray[5] 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_fuse_getter_with_asarray[5] 
dask/array/tests/test_optimization.py::test_fuse_getter_with_asarray[3] 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_fuse_getter_with_asarray[3] 
dask/array/tests/test_optimization.py::test_turn_off_fusion 
[gw1] [ 19%] XFAIL dask/array/tests/test_optimization.py::test_turn_off_fusion 
dask/array/tests/test_optimization.py::test_disable_lowlevel_fusion 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_disable_lowlevel_fusion 
dask/array/tests/test_optimization.py::test_array_creation_blockwise_fusion 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_array_creation_blockwise_fusion 
dask/array/tests/test_optimization.py::test_gh3937 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_gh3937 
dask/array/tests/test_optimization.py::test_double_dependencies 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_double_dependencies 
dask/array/tests/test_optimization.py::test_fuse_roots 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_fuse_roots 
dask/array/tests/test_optimization.py::test_fuse_roots_annotations 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_fuse_roots_annotations 
dask/array/tests/test_optimization.py::test_optimize_blockwise_duplicate_dependency[True] 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_optimize_blockwise_duplicate_dependency[True] 
dask/array/tests/test_optimization.py::test_optimize_blockwise_duplicate_dependency[False] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-1] 
[gw1] [ 19%] PASSED dask/array/tests/test_optimization.py::test_optimize_blockwise_duplicate_dependency[False] 
dask/array/tests/test_overlap.py::test_overlap_internal 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-index] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_overlap_internal 
dask/array/tests/test_overlap.py::test_overlap_internal_asymmetric 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_overlap_internal_asymmetric 
dask/array/tests/test_overlap.py::test_overlap_internal_asymmetric_small 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_overlap_internal_asymmetric_small 
dask/array/tests/test_overlap.py::test_trim_internal 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_trim_internal 
dask/array/tests/test_overlap.py::test_periodic 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_periodic 
dask/array/tests/test_overlap.py::test_reflect 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_reflect 
dask/array/tests/test_overlap.py::test_nearest 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_nearest 
dask/array/tests/test_overlap.py::test_constant 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_constant 
dask/array/tests/test_overlap.py::test_boundaries 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_boundaries 
dask/array/tests/test_overlap.py::test_overlap 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_overlap 
dask/array/tests/test_overlap.py::test_overlap_allow_rechunk_kwarg 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_overlap_allow_rechunk_kwarg 
dask/array/tests/test_overlap.py::test_asymmetric_overlap_boundary_exception 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_asymmetric_overlap_boundary_exception 
dask/array/tests/test_overlap.py::test_map_overlap 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap 
dask/array/tests/test_overlap.py::test_map_overlap_escapes_to_map_blocks_when_depth_is_zero 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_escapes_to_map_blocks_when_depth_is_zero 
dask/array/tests/test_overlap.py::test_map_overlap_no_depth[None] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_no_depth[None] 
dask/array/tests/test_overlap.py::test_map_overlap_no_depth[reflect] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_no_depth[reflect] 
dask/array/tests/test_overlap.py::test_map_overlap_no_depth[periodic] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_no_depth[periodic] 
dask/array/tests/test_overlap.py::test_map_overlap_no_depth[nearest] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_no_depth[nearest] 
dask/array/tests/test_overlap.py::test_map_overlap_no_depth[none] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_no_depth[none] 
dask/array/tests/test_overlap.py::test_map_overlap_no_depth[0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_no_depth[0] 
dask/array/tests/test_overlap.py::test_map_overlap_multiarray 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_multiarray 
dask/array/tests/test_overlap.py::test_map_overlap_multiarray_defaults 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_multiarray_defaults 
dask/array/tests/test_overlap.py::test_map_overlap_multiarray_different_depths 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-columns] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_multiarray_different_depths 
dask/array/tests/test_overlap.py::test_map_overlap_multiarray_uneven_numblocks_exception 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_multiarray_uneven_numblocks_exception 
dask/array/tests/test_overlap.py::test_map_overlap_multiarray_block_broadcast 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_multiarray_block_broadcast 
dask/array/tests/test_overlap.py::test_map_overlap_multiarray_variadic 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-1] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_multiarray_variadic 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis0] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-index] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis0] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis1] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis1] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis2] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis2] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis3] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis3] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis4] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis4] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis5] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis5] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[1] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[1] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis7] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-columns] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis7] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis8] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis8] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis9] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis9] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis10] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-1] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis10] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis11] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis11] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis12] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[drop_axis12] 
dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[-2] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-1] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_using_drop_axis_and_different_depths[-2] 
dask/array/tests/test_overlap.py::test_map_overlap_assumes_shape_matches_first_array_if_trim_is_false 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-index] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_assumes_shape_matches_first_array_if_trim_is_false 
dask/array/tests/test_overlap.py::test_map_overlap_deprecated_signature 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_deprecated_signature 
dask/array/tests/test_overlap.py::test_map_overlap_trim_false_chunking 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_trim_false_chunking 
dask/array/tests/test_overlap.py::test_nearest_overlap 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_nearest_overlap 
dask/array/tests/test_overlap.py::test_different_depths_and_boundary_combinations[depth0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_different_depths_and_boundary_combinations[depth0] 
dask/array/tests/test_overlap.py::test_different_depths_and_boundary_combinations[depth1] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_different_depths_and_boundary_combinations[depth1] 
dask/array/tests/test_overlap.py::test_different_depths_and_boundary_combinations[depth2] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_different_depths_and_boundary_combinations[depth2] 
dask/array/tests/test_overlap.py::test_different_depths_and_boundary_combinations[depth3] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_different_depths_and_boundary_combinations[depth3] 
dask/array/tests/test_overlap.py::test_one_chunk_along_axis 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_one_chunk_along_axis 
dask/array/tests/test_overlap.py::test_constant_boundaries 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_constant_boundaries 
dask/array/tests/test_overlap.py::test_depth_greater_than_smallest_chunk_combines_chunks[chunks0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_depth_greater_than_smallest_chunk_combines_chunks[chunks0] 
dask/array/tests/test_overlap.py::test_depth_greater_than_smallest_chunk_combines_chunks[chunks1] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_depth_greater_than_smallest_chunk_combines_chunks[chunks1] 
dask/array/tests/test_overlap.py::test_depth_greater_than_dim 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_depth_greater_than_dim 
dask/array/tests/test_overlap.py::test_none_boundaries 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_none_boundaries 
dask/array/tests/test_overlap.py::test_overlap_small 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_overlap_small 
dask/array/tests/test_overlap.py::test_no_shared_keys_with_different_depths 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-columns] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_no_shared_keys_with_different_depths 
dask/array/tests/test_overlap.py::test_overlap_few_dimensions_small 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_overlap_few_dimensions_small 
dask/array/tests/test_overlap.py::test_overlap_few_dimensions 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_overlap_few_dimensions 
dask/array/tests/test_overlap.py::test_push 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-1] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-index] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-0] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-columns] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-1] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_push 
dask/array/tests/test_overlap.py::test_trim_boundary[reflect] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_trim_boundary[reflect] 
dask/array/tests/test_overlap.py::test_trim_boundary[periodic] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-index] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_trim_boundary[periodic] 
dask/array/tests/test_overlap.py::test_trim_boundary[nearest] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_trim_boundary[nearest] 
dask/array/tests/test_overlap.py::test_trim_boundary[none] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_trim_boundary[none] 
dask/array/tests/test_overlap.py::test_map_overlap_rechunks_array_if_needed 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_rechunks_array_if_needed 
dask/array/tests/test_overlap.py::test_map_overlap_rechunks_array_along_multiple_dims_if_needed 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-0] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-columns] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_rechunks_array_along_multiple_dims_if_needed 
dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks0-expected0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks0-expected0] 
dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks1-expected1] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks1-expected1] 
dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks2-expected2] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks2-expected2] 
dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks3-expected3] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks3-expected3] 
dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks4-expected4] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks4-expected4] 
dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks5-expected5] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks5-expected5] 
dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks6-expected6] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks6-expected6] 
dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks7-expected7] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize[chunks7-expected7] 
dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize_raises_error 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_ensure_minimum_chunksize_raises_error 
dask/array/tests/test_overlap.py::test_sliding_window_view[shape0-chunks0-window_shape0-axis0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view[shape0-chunks0-window_shape0-axis0] 
dask/array/tests/test_overlap.py::test_sliding_window_view[shape1-5-window_shape1-axis1] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-1] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-index] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view[shape1-5-window_shape1-axis1] 
dask/array/tests/test_overlap.py::test_sliding_window_view[shape2-3-window_shape2-axis2] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view[shape2-3-window_shape2-axis2] 
dask/array/tests/test_overlap.py::test_sliding_window_view[shape3-3-3-0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view[shape3-3-3-0] 
dask/array/tests/test_overlap.py::test_sliding_window_view[shape4-3-3--1] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view[shape4-3-3--1] 
dask/array/tests/test_overlap.py::test_sliding_window_view[shape5-3-3-None] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view[shape5-3-3-None] 
dask/array/tests/test_overlap.py::test_sliding_window_view[shape6-3-window_shape6-None] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view[shape6-3-window_shape6-None] 
dask/array/tests/test_overlap.py::test_sliding_window_view[shape7-3-window_shape7-axis7] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view[shape7-3-window_shape7-axis7] 
dask/array/tests/test_overlap.py::test_sliding_window_view[shape8-3-window_shape8-axis8] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view[shape8-3-window_shape8-axis8] 
dask/array/tests/test_overlap.py::test_sliding_window_view[shape9-3-window_shape9-axis9] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view[shape9-3-window_shape9-axis9] 
dask/array/tests/test_overlap.py::test_sliding_window_errors[window_shape0-0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_errors[window_shape0-0] 
dask/array/tests/test_overlap.py::test_sliding_window_errors[window_shape1-3] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_errors[window_shape1-3] 
dask/array/tests/test_overlap.py::test_sliding_window_errors[-1-0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_errors[-1-0] 
dask/array/tests/test_overlap.py::test_sliding_window_errors[2-axis3] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_errors[2-axis3] 
dask/array/tests/test_overlap.py::test_sliding_window_errors[2-None] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_errors[2-None] 
dask/array/tests/test_overlap.py::test_sliding_window_errors[0-None] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_errors[0-None] 
dask/array/tests/test_overlap.py::test_sliding_window_view_chunking[output_chunks0-5-0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view_chunking[output_chunks0-5-0] 
dask/array/tests/test_overlap.py::test_sliding_window_view_chunking[output_chunks1-20-0] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view_chunking[output_chunks1-20-0] 
dask/array/tests/test_overlap.py::test_sliding_window_view_chunking[output_chunks2-40-0] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-columns] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-1] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view_chunking[output_chunks2-40-0] 
dask/array/tests/test_overlap.py::test_sliding_window_view_chunking[output_chunks3-window_shape3-axis3] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view_chunking[output_chunks3-window_shape3-axis3] 
dask/array/tests/test_overlap.py::test_sliding_window_view_chunking[output_chunks4-window_shape4-axis4] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-index] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view_chunking[output_chunks4-window_shape4-axis4] 
dask/array/tests/test_overlap.py::test_sliding_window_view_no_chunking 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-0] 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_sliding_window_view_no_chunking 
dask/array/tests/test_overlap.py::test_overlap_not_adding_empty_tasks 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_overlap_not_adding_empty_tasks 
dask/array/tests/test_overlap.py::test_map_overlap_new_axis 
[gw1] [ 19%] PASSED dask/array/tests/test_overlap.py::test_map_overlap_new_axis 
dask/array/tests/test_overlap.py::test_overlap_not_blowing_up_graph 
[gw1] [ 19%] SKIPPED dask/array/tests/test_overlap.py::test_overlap_not_blowing_up_graph 
dask/array/tests/test_percentiles.py::test_percentile[tdigest] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentile[tdigest] 
dask/array/tests/test_percentiles.py::test_percentile[dask] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentile[dask] 
dask/array/tests/test_percentiles.py::test_percentile_with_categoricals 
[gw1] [ 19%] SKIPPED dask/array/tests/test_percentiles.py::test_percentile_with_categoricals 
dask/array/tests/test_percentiles.py::test_percentiles_with_empty_arrays[tdigest] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_empty_arrays[tdigest] 
dask/array/tests/test_percentiles.py::test_percentiles_with_empty_arrays[dask] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_empty_arrays[dask] 
dask/array/tests/test_percentiles.py::test_percentiles_with_empty_q[tdigest] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_empty_q[tdigest] 
dask/array/tests/test_percentiles.py::test_percentiles_with_empty_q[dask] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_empty_q[dask] 
dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5-tdigest] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5-tdigest] 
dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5-dask] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5-dask] 
dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5.0_0-tdigest] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5.0_0-tdigest] 
dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5.0_0-dask] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5.0_0-dask] 
dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[q2-tdigest] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[q2-tdigest] 
dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[q2-dask] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[q2-dask] 
dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5.0_1-tdigest] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5.0_1-tdigest] 
dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5.0_1-dask] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_percentiles_with_scaler_percentile[5.0_1-dask] 
dask/array/tests/test_percentiles.py::test_unknown_chunk_sizes[tdigest] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-columns] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_unknown_chunk_sizes[tdigest] 
dask/array/tests/test_percentiles.py::test_unknown_chunk_sizes[dask] 
[gw1] [ 19%] PASSED dask/array/tests/test_percentiles.py::test_unknown_chunk_sizes[dask] 
dask/array/tests/test_random.py::test_generators[RandomState] 
[gw1] [ 19%] PASSED dask/array/tests/test_random.py::test_generators[RandomState] 
dask/array/tests/test_random.py::test_generators[default_rng] 
[gw1] [ 19%] PASSED dask/array/tests/test_random.py::test_generators[default_rng] 
dask/array/tests/test_random.py::test_default_rng[NoneType] 
[gw1] [ 19%] PASSED dask/array/tests/test_random.py::test_default_rng[NoneType] 
dask/array/tests/test_random.py::test_default_rng[int] 
[gw1] [ 19%] PASSED dask/array/tests/test_random.py::test_default_rng[int] 
dask/array/tests/test_random.py::test_default_rng[type] 
[gw1] [ 19%] PASSED dask/array/tests/test_random.py::test_default_rng[type] 
dask/array/tests/test_random.py::test_default_rng[Generator] 
[gw1] [ 19%] PASSED dask/array/tests/test_random.py::test_default_rng[Generator] 
dask/array/tests/test_random.py::test_concurrency[RandomState] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-1] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-index] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-0] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-columns] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-1] 
[gw0] [ 19%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-index] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_concurrency[RandomState] 
dask/array/tests/test_random.py::test_concurrency[default_rng] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-0] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-columns] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-1] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-index] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-0] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-columns] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_concurrency[default_rng] 
dask/array/tests/test_random.py::test_doc_randomstate[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_doc_randomstate[RandomState] 
dask/array/tests/test_random.py::test_doc_randomstate[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_doc_randomstate[default_rng] 
dask/array/tests/test_random.py::test_doc_generator 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_doc_generator 
dask/array/tests/test_random.py::test_serializability[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_serializability[RandomState] 
dask/array/tests/test_random.py::test_serializability[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_serializability[default_rng] 
dask/array/tests/test_random.py::test_determinisim_through_dask_values[RandomState] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-1] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_determinisim_through_dask_values[RandomState] 
dask/array/tests/test_random.py::test_determinisim_through_dask_values[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_determinisim_through_dask_values[default_rng] 
dask/array/tests/test_random.py::test_generator_consistent_names[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_generator_consistent_names[RandomState] 
dask/array/tests/test_random.py::test_generator_consistent_names[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_generator_consistent_names[default_rng] 
dask/array/tests/test_random.py::test_random[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random[RandomState] 
dask/array/tests/test_random.py::test_random[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random[default_rng] 
dask/array/tests/test_random.py::test_parametrized_random_function[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_parametrized_random_function[RandomState] 
dask/array/tests/test_random.py::test_parametrized_random_function[default_rng] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-1] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_parametrized_random_function[default_rng] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-index] 
dask/array/tests/test_random.py::test_kwargs[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_kwargs[RandomState] 
dask/array/tests/test_random.py::test_kwargs[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_kwargs[default_rng] 
dask/array/tests/test_random.py::test_unique_names[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_unique_names[RandomState] 
dask/array/tests/test_random.py::test_unique_names[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_unique_names[default_rng] 
dask/array/tests/test_random.py::test_docs[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_docs[RandomState] 
dask/array/tests/test_random.py::test_docs[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_docs[default_rng] 
dask/array/tests/test_random.py::test_can_make_really_big_random_array[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_can_make_really_big_random_array[RandomState] 
dask/array/tests/test_random.py::test_can_make_really_big_random_array[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_can_make_really_big_random_array[default_rng] 
dask/array/tests/test_random.py::test_random_seed 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random_seed 
dask/array/tests/test_random.py::test_consistent_across_sizes[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_consistent_across_sizes[RandomState] 
dask/array/tests/test_random.py::test_consistent_across_sizes[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_consistent_across_sizes[default_rng] 
dask/array/tests/test_random.py::test_random_all[NoneType] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-0] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random_all[NoneType] 
dask/array/tests/test_random.py::test_random_all[int] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random_all[int] 
dask/array/tests/test_random.py::test_random_all[tuple] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-columns] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random_all[tuple] 
dask/array/tests/test_random.py::test_RandomState_only_funcs 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_RandomState_only_funcs 
dask/array/tests/test_random.py::test_Generator_only_funcs[NoneType] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_Generator_only_funcs[NoneType] 
dask/array/tests/test_random.py::test_Generator_only_funcs[int] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_Generator_only_funcs[int] 
dask/array/tests/test_random.py::test_Generator_only_funcs[tuple] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_Generator_only_funcs[tuple] 
dask/array/tests/test_random.py::test_random_all_with_class_methods[RandomState-NoneType] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-1] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random_all_with_class_methods[RandomState-NoneType] 
dask/array/tests/test_random.py::test_random_all_with_class_methods[RandomState-int] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-index] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random_all_with_class_methods[RandomState-int] 
dask/array/tests/test_random.py::test_random_all_with_class_methods[RandomState-tuple] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-0] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-columns] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random_all_with_class_methods[RandomState-tuple] 
dask/array/tests/test_random.py::test_random_all_with_class_methods[default_rng-NoneType] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-1] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random_all_with_class_methods[default_rng-NoneType] 
dask/array/tests/test_random.py::test_random_all_with_class_methods[default_rng-int] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-index] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random_all_with_class_methods[default_rng-int] 
dask/array/tests/test_random.py::test_random_all_with_class_methods[default_rng-tuple] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-0] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_random_all_with_class_methods[default_rng-tuple] 
dask/array/tests/test_random.py::test_array_broadcasting[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_array_broadcasting[RandomState] 
dask/array/tests/test_random.py::test_array_broadcasting[default_rng] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-columns] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_array_broadcasting[default_rng] 
dask/array/tests/test_random.py::test_multinomial[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_multinomial[RandomState] 
dask/array/tests/test_random.py::test_multinomial[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_multinomial[default_rng] 
dask/array/tests/test_random.py::test_choice[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_choice[RandomState] 
dask/array/tests/test_random.py::test_choice[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_choice[default_rng] 
dask/array/tests/test_random.py::test_create_with_auto_dimensions 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_create_with_auto_dimensions 
dask/array/tests/test_random.py::test_names 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_names 
dask/array/tests/test_random.py::test_permutation[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_permutation[RandomState] 
dask/array/tests/test_random.py::test_permutation[default_rng] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-1] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_permutation[default_rng] 
dask/array/tests/test_random.py::test_auto_chunks[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_auto_chunks[RandomState] 
dask/array/tests/test_random.py::test_auto_chunks[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_auto_chunks[default_rng] 
dask/array/tests/test_random.py::test_randint_dtype 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_randint_dtype 
dask/array/tests/test_random.py::test_raises_bad_kwarg[RandomState] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_raises_bad_kwarg[RandomState] 
dask/array/tests/test_random.py::test_raises_bad_kwarg[default_rng] 
[gw1] [ 20%] PASSED dask/array/tests/test_random.py::test_raises_bad_kwarg[default_rng] 
dask/array/tests/test_random.py::test_randomstate_kwargs 
[gw1] [ 20%] SKIPPED dask/array/tests/test_random.py::test_randomstate_kwargs 
dask/array/tests/test_rechunk.py::test_rechunk_internals_1 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_internals_1 
dask/array/tests/test_rechunk.py::test_intersect_1 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_intersect_1 
dask/array/tests/test_rechunk.py::test_intersect_2 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_intersect_2 
dask/array/tests/test_rechunk.py::test_rechunk_1d 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_1d 
dask/array/tests/test_rechunk.py::test_rechunk_2d 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_2d 
dask/array/tests/test_rechunk.py::test_rechunk_4d 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_4d 
dask/array/tests/test_rechunk.py::test_rechunk_expand 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_expand 
dask/array/tests/test_rechunk.py::test_rechunk_expand2 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_expand2 
dask/array/tests/test_rechunk.py::test_rechunk_method 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-index] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_method 
dask/array/tests/test_rechunk.py::test_rechunk_blockshape 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_blockshape 
dask/array/tests/test_rechunk.py::test_dtype 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_dtype 
dask/array/tests/test_rechunk.py::test_rechunk_with_dict 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_dict 
dask/array/tests/test_rechunk.py::test_rechunk_with_empty_input 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_empty_input 
dask/array/tests/test_rechunk.py::test_rechunk_with_null_dimensions 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_null_dimensions 
dask/array/tests/test_rechunk.py::test_rechunk_with_integer 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_integer 
dask/array/tests/test_rechunk.py::test_rechunk_0d 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_0d 
dask/array/tests/test_rechunk.py::test_rechunk_empty_array[arr0] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_empty_array[arr0] 
dask/array/tests/test_rechunk.py::test_rechunk_empty_array[arr1] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_empty_array[arr1] 
dask/array/tests/test_rechunk.py::test_rechunk_empty_array[arr2] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_empty_array[arr2] 
dask/array/tests/test_rechunk.py::test_rechunk_empty 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_empty 
dask/array/tests/test_rechunk.py::test_rechunk_zero_dim_array 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_zero_dim_array 
dask/array/tests/test_rechunk.py::test_rechunk_zero_dim_array_II 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_zero_dim_array_II 
dask/array/tests/test_rechunk.py::test_rechunk_same 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_same 
dask/array/tests/test_rechunk.py::test_rechunk_same_fully_unknown 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-0] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_same_fully_unknown 
dask/array/tests/test_rechunk.py::test_rechunk_same_fully_unknown_floats 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_same_fully_unknown_floats 
dask/array/tests/test_rechunk.py::test_rechunk_same_partially_unknown 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_same_partially_unknown 
dask/array/tests/test_rechunk.py::test_rechunk_with_zero_placeholders 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_zero_placeholders 
dask/array/tests/test_rechunk.py::test_rechunk_minus_one 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_minus_one 
dask/array/tests/test_rechunk.py::test_rechunk_intermediates 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_intermediates 
dask/array/tests/test_rechunk.py::test_divide_to_width 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_divide_to_width 
dask/array/tests/test_rechunk.py::test_merge_to_number 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_merge_to_number 
dask/array/tests/test_rechunk.py::test_plan_rechunk 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_plan_rechunk 
dask/array/tests/test_rechunk.py::test_plan_rechunk_5d 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_plan_rechunk_5d 
dask/array/tests/test_rechunk.py::test_plan_rechunk_heterogeneous 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_plan_rechunk_heterogeneous 
dask/array/tests/test_rechunk.py::test_plan_rechunk_asymmetric 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-columns] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-1] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-index] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-0] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_plan_rechunk_asymmetric 
dask/array/tests/test_rechunk.py::test_rechunk_warning 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_warning 
dask/array/tests/test_rechunk.py::test_dont_concatenate_single_chunks[shape0-chunks0] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_dont_concatenate_single_chunks[shape0-chunks0] 
dask/array/tests/test_rechunk.py::test_dont_concatenate_single_chunks[shape1-chunks1] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_dont_concatenate_single_chunks[shape1-chunks1] 
dask/array/tests/test_rechunk.py::test_dont_concatenate_single_chunks[shape2-chunks2] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_dont_concatenate_single_chunks[shape2-chunks2] 
dask/array/tests/test_rechunk.py::test_intersect_nan 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_intersect_nan 
dask/array/tests/test_rechunk.py::test_intersect_nan_single 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_intersect_nan_single 
dask/array/tests/test_rechunk.py::test_intersect_nan_long 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_intersect_nan_long 
dask/array/tests/test_rechunk.py::test_rechunk_unknown_from_pandas 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_unknown_from_pandas 
dask/array/tests/test_rechunk.py::test_rechunk_unknown_from_array 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_unknown_from_array 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x0-chunks0] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x0-chunks0] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x1-chunks1] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x1-chunks1] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x2-chunks2] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x2-chunks2] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x3-chunks3] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-columns] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-1] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-index] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-0] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x3-chunks3] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x4-chunks4] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-columns] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-1] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-index] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-0] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-columns] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x4-chunks4] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x5-chunks5] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-1] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-index] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-0] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-columns] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-1] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x5-chunks5] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x6-chunks6] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x6-chunks6] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x7-chunks7] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x7-chunks7] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x8-chunks8] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x8-chunks8] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x9-chunks9] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x9-chunks9] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x10-chunks10] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x10-chunks10] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x11-chunks11] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension[x11-chunks11] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x0-chunks0] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-index] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x0-chunks0] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x1-chunks1] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x1-chunks1] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x2-chunks2] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x2-chunks2] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x3-chunks3] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-0] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-columns] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-1] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-index] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-0] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x3-chunks3] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x4-chunks4] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-columns] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-1] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-index] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-0] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-columns] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-1] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-index] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x4-chunks4] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x5-chunks5] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-0] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-columns] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-1] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-index] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-0] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-columns] 
[gw0] [ 20%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-1] 
[gw1] [ 20%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x5-chunks5] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x6-chunks6] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x6-chunks6] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x7-chunks7] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x7-chunks7] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x8-chunks8] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x8-chunks8] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x9-chunks9] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x9-chunks9] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x10-chunks10] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x10-chunks10] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x11-chunks11] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension[x11-chunks11] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension_explicit[new_chunks0] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension_explicit[new_chunks0] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension_explicit[new_chunks1] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension_explicit[new_chunks1] 
dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension_explicit[new_chunks2] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-index] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_fully_unknown_dimension_explicit[new_chunks2] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension_explicit[new_chunks0] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension_explicit[new_chunks0] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension_explicit[new_chunks1] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension_explicit[new_chunks1] 
dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension_explicit[new_chunks2] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_partially_unknown_dimension_explicit[new_chunks2] 
dask/array/tests/test_rechunk.py::test_rechunk_unknown_raises 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_unknown_raises 
dask/array/tests/test_rechunk.py::test_old_to_new_single 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_old_to_new_single 
dask/array/tests/test_rechunk.py::test_old_to_new 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_old_to_new 
dask/array/tests/test_rechunk.py::test_old_to_new_large 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_old_to_new_large 
dask/array/tests/test_rechunk.py::test_old_to_new_known 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_old_to_new_known 
dask/array/tests/test_rechunk.py::test_rechunk_zero_dim 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-0] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_zero_dim 
dask/array/tests/test_rechunk.py::test_rechunk_empty_chunks 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_empty_chunks 
dask/array/tests/test_rechunk.py::test_rechunk_avoid_needless_chunking 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_avoid_needless_chunking 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[100-1-10-expected0] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[100-1-10-expected0] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[100-50-10-expected1] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[100-50-10-expected1] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[100-100-10-expected2] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[100-100-10-expected2] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[20-7-10-expected3] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[20-7-10-expected3] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[20-chunks4-5-expected4] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[20-chunks4-5-expected4] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[21-chunks5-10-expected5] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[21-chunks5-10-expected5] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[30-chunks6-7-expected6] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[30-chunks6-7-expected6] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[20-chunks7-5-expected7] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[20-chunks7-5-expected7] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[20-chunks8-4-expected8] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[20-chunks8-4-expected8] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[21-chunks9-4-expected9] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[21-chunks9-4-expected9] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[20-chunks10-5-expected10] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[20-chunks10-5-expected10] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[38-chunks11-5-expected11] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_1d[38-chunks11-5-expected11] 
dask/array/tests/test_rechunk.py::test_normalize_chunks_auto_2d[previous_chunks0-160-expected0] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_normalize_chunks_auto_2d[previous_chunks0-160-expected0] 
dask/array/tests/test_rechunk.py::test_normalize_chunks_auto_2d[previous_chunks1-5-expected1] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_normalize_chunks_auto_2d[previous_chunks1-5-expected1] 
dask/array/tests/test_rechunk.py::test_normalize_chunks_auto_2d[previous_chunks2-5-expected2] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_normalize_chunks_auto_2d[previous_chunks2-5-expected2] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_2d 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_2d 
dask/array/tests/test_rechunk.py::test_rechunk_auto_3d 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_3d 
dask/array/tests/test_rechunk.py::test_rechunk_auto_image_stack[100] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-columns] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_image_stack[100] 
dask/array/tests/test_rechunk.py::test_rechunk_auto_image_stack[1000] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-1] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-index] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-0] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-columns] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-1] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-index] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-0] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-columns] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-1] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-index] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_auto_image_stack[1000] 
dask/array/tests/test_rechunk.py::test_rechunk_down 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_down 
dask/array/tests/test_rechunk.py::test_rechunk_zero 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_zero 
dask/array/tests/test_rechunk.py::test_rechunk_bad_keys 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_bad_keys 
dask/array/tests/test_rechunk.py::test_balance_basics 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_balance_basics 
dask/array/tests/test_rechunk.py::test_balance_chunks_unchanged 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_balance_chunks_unchanged 
dask/array/tests/test_rechunk.py::test_balance_small 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_balance_small 
dask/array/tests/test_rechunk.py::test_balance_n_chunks_size 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_balance_n_chunks_size 
dask/array/tests/test_rechunk.py::test_balance_raises 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_balance_raises 
dask/array/tests/test_rechunk.py::test_balance_basics_2d 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_balance_basics_2d 
dask/array/tests/test_rechunk.py::test_balance_2d_negative_dimension 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_balance_2d_negative_dimension 
dask/array/tests/test_rechunk.py::test_balance_different_inputs 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_balance_different_inputs 
dask/array/tests/test_rechunk.py::test_balance_split_into_n_chunks 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-0] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-columns] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_balance_split_into_n_chunks 
dask/array/tests/test_rechunk.py::test_rechunk_with_zero 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_with_zero 
dask/array/tests/test_rechunk.py::test_intersect_chunks_with_nonzero 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_intersect_chunks_with_nonzero 
dask/array/tests/test_rechunk.py::test_intersect_chunks_with_zero 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_intersect_chunks_with_zero 
dask/array/tests/test_rechunk.py::test_old_to_new_with_zero 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_old_to_new_with_zero 
dask/array/tests/test_rechunk.py::test_rechunk_non_perfect_slicing_of_dimensions 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-1] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-index] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-0] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-columns] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-1] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-index] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-0] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-columns] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-1] 
[gw1] [ 21%] PASSED dask/array/tests/test_rechunk.py::test_rechunk_non_perfect_slicing_of_dimensions 
dask/array/tests/test_reductions.py::test_numel[True-True-f4] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_numel[True-True-f4] 
dask/array/tests/test_reductions.py::test_numel[True-True-i4] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_numel[True-True-i4] 
dask/array/tests/test_reductions.py::test_numel[True-False-f4] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_numel[True-False-f4] 
dask/array/tests/test_reductions.py::test_numel[True-False-i4] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_numel[True-False-i4] 
dask/array/tests/test_reductions.py::test_numel[False-True-f4] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_numel[False-True-f4] 
dask/array/tests/test_reductions.py::test_numel[False-True-i4] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_numel[False-True-i4] 
dask/array/tests/test_reductions.py::test_numel[False-False-f4] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_numel[False-False-f4] 
dask/array/tests/test_reductions.py::test_numel[False-False-i4] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_numel[False-False-i4] 
dask/array/tests/test_reductions.py::test_reductions_0D 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-index] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_reductions_0D 
dask/array/tests/test_reductions.py::test_reductions_1D[f4] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-0] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-columns] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-1] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-index] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_reductions_1D[f4] 
dask/array/tests/test_reductions.py::test_reductions_1D[i4] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-0] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-columns] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-1] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_reductions_1D[i4] 
dask/array/tests/test_reductions.py::test_reductions_1D[c8] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-index] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-0] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-columns] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-1] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-index] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_reductions_1D[c8] 
dask/array/tests/test_reductions.py::test_reduction_errors 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_reduction_errors 
dask/array/tests/test_reductions.py::test_reductions_2D[f4] 
[gw1] [ 21%] SKIPPED dask/array/tests/test_reductions.py::test_reductions_2D[f4] 
dask/array/tests/test_reductions.py::test_reductions_2D[i4] 
[gw1] [ 21%] SKIPPED dask/array/tests/test_reductions.py::test_reductions_2D[i4] 
dask/array/tests/test_reductions.py::test_reductions_2D[c8] 
[gw1] [ 21%] SKIPPED dask/array/tests/test_reductions.py::test_reductions_2D[c8] 
dask/array/tests/test_reductions.py::test_arg_reductions[argmin-argmin] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_arg_reductions[argmin-argmin] 
dask/array/tests/test_reductions.py::test_arg_reductions[argmax-argmax] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-0] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_arg_reductions[argmax-argmax] 
dask/array/tests/test_reductions.py::test_arg_reductions[nanargmin-nanargmin] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-columns] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_arg_reductions[nanargmin-nanargmin] 
dask/array/tests/test_reductions.py::test_arg_reductions[nanargmax-nanargmax] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-1] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_arg_reductions[nanargmax-nanargmax] 
dask/array/tests/test_reductions.py::test_nan_reduction_warnings[nanmin-nanmin] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_nan_reduction_warnings[nanmin-nanmin] 
dask/array/tests/test_reductions.py::test_nan_reduction_warnings[nanmax-nanmax] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_nan_reduction_warnings[nanmax-nanmax] 
dask/array/tests/test_reductions.py::test_nanarg_reductions[nanargmin-nanargmin] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_nanarg_reductions[nanargmin-nanargmin] 
dask/array/tests/test_reductions.py::test_nanarg_reductions[nanargmax-nanargmax] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-1] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_nanarg_reductions[nanargmax-nanargmax] 
dask/array/tests/test_reductions.py::test_min_max_empty_chunks[min-min] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-index] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_min_max_empty_chunks[min-min] 
dask/array/tests/test_reductions.py::test_min_max_empty_chunks[max-max] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_min_max_empty_chunks[max-max] 
dask/array/tests/test_reductions.py::test_arg_reductions_unknown_chunksize[argmax] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_arg_reductions_unknown_chunksize[argmax] 
dask/array/tests/test_reductions.py::test_arg_reductions_unknown_chunksize[nanargmax] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_arg_reductions_unknown_chunksize[nanargmax] 
dask/array/tests/test_reductions.py::test_arg_reductions_unknown_chunksize_2d[argmax] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_arg_reductions_unknown_chunksize_2d[argmax] 
dask/array/tests/test_reductions.py::test_arg_reductions_unknown_chunksize_2d[nanargmax] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_arg_reductions_unknown_chunksize_2d[nanargmax] 
dask/array/tests/test_reductions.py::test_arg_reductions_unknown_single_chunksize[argmax] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_arg_reductions_unknown_single_chunksize[argmax] 
dask/array/tests/test_reductions.py::test_arg_reductions_unknown_single_chunksize[nanargmax] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_arg_reductions_unknown_single_chunksize[nanargmax] 
dask/array/tests/test_reductions.py::test_reductions_2D_nans 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-0] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-columns] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-1] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-index] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-0] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-columns] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_reductions_2D_nans 
dask/array/tests/test_reductions.py::test_moment 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_moment 
dask/array/tests/test_reductions.py::test_reductions_with_negative_axes 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_reductions_with_negative_axes 
dask/array/tests/test_reductions.py::test_nan 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_nan 
dask/array/tests/test_reductions.py::test_nan_object[nansum] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-1] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_nan_object[nansum] 
dask/array/tests/test_reductions.py::test_nan_object[sum] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_nan_object[sum] 
dask/array/tests/test_reductions.py::test_nan_object[nanmin] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_nan_object[nanmin] 
dask/array/tests/test_reductions.py::test_nan_object[min] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_nan_object[min] 
dask/array/tests/test_reductions.py::test_nan_object[nanmax] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_nan_object[nanmax] 
dask/array/tests/test_reductions.py::test_nan_object[max] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_nan_object[max] 
dask/array/tests/test_reductions.py::test_0d_array 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_0d_array 
dask/array/tests/test_reductions.py::test_reduction_on_scalar 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_reduction_on_scalar 
dask/array/tests/test_reductions.py::test_reductions_with_empty_array 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-index] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_reductions_with_empty_array 
dask/array/tests/test_reductions.py::test_tree_reduce_depth 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-0] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_tree_reduce_depth 
dask/array/tests/test_reductions.py::test_tree_reduce_set_options 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_tree_reduce_set_options 
dask/array/tests/test_reductions.py::test_reduction_names 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_reduction_names 
dask/array/tests/test_reductions.py::test_general_reduction_names 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_general_reduction_names 
dask/array/tests/test_reductions.py::test_array_reduction_out[sum] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_reduction_out[sum] 
dask/array/tests/test_reductions.py::test_array_reduction_out[argmax] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_reduction_out[argmax] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-False-cumsum] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-False-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-False-cumprod] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-columns] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-False-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-False-nancumsum] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-False-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-False-nancumprod] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-False-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-True-cumsum] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-1] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-index] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-True-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-True-cumprod] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-True-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-True-nancumsum] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-True-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-True-nancumprod] 
[gw0] [ 21%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-0] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-None-True-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-False-cumsum] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-False-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-False-cumprod] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-False-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-False-nancumsum] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-False-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-False-nancumprod] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-False-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-True-cumsum] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-True-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-True-cumprod] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-True-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-True-nancumsum] 
[gw1] [ 21%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-True-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-True-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-0-True-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-False-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-False-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-False-cumprod] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-columns] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-False-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-False-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-False-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-False-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-False-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-True-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-True-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-True-cumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-True-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-True-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-True-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-True-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential-1-True-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-False-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-False-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-False-cumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-False-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-False-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-False-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-False-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-False-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-True-cumsum] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-1] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-True-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-True-cumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-True-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-True-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-True-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-True-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[sequential--1-True-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-False-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-False-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-False-cumprod] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-index] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-False-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-False-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-False-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-False-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-False-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-True-cumsum] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-0] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-True-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-True-cumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-True-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-True-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-True-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-True-nancumprod] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-columns] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-None-True-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-False-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-False-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-False-cumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-False-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-False-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-False-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-False-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-False-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-True-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-True-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-True-cumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-True-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-True-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-True-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-True-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-0-True-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-False-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-False-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-False-cumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-False-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-False-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-False-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-False-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-False-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-True-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-True-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-True-cumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-True-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-True-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-True-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-True-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch-1-True-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-False-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-False-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-False-cumprod] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-1] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-False-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-False-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-False-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-False-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-False-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-True-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-True-cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-True-cumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-True-cumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-True-nancumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-True-nancumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-True-nancumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_axis[blelloch--1-True-nancumprod] 
dask/array/tests/test_reductions.py::test_array_cumreduction_out[cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_out[cumsum] 
dask/array/tests/test_reductions.py::test_array_cumreduction_out[cumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_array_cumreduction_out[cumprod] 
dask/array/tests/test_reductions.py::test_topk_argtopk1[None-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk1[None-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk1[None-argsort-argtopk] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-index] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk1[None-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk1[2-sort-topk] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-0] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk1[2-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk1[2-argsort-argtopk] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-columns] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk1[2-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk1[4-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk1[4-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk1[4-argsort-argtopk] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-1] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk1[4-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk1[8-sort-topk] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-index] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk1[8-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk1[8-argsort-argtopk] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-0] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk1[8-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[1-None-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[1-None-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[1-None-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[1-None-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[1-2-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[1-2-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[1-2-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[1-2-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[1-3-sort-topk] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-columns] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[1-3-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[1-3-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[1-3-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[1-4-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[1-4-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[1-4-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[1-4-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[2-None-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[2-None-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[2-None-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[2-None-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[2-2-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[2-2-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[2-2-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[2-2-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[2-3-sort-topk] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-1] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[2-3-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[2-3-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[2-3-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[2-4-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[2-4-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[2-4-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[2-4-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[3-None-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[3-None-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[3-None-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[3-None-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[3-2-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[3-2-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[3-2-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[3-2-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[3-3-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[3-3-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[3-3-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[3-3-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[3-4-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[3-4-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[3-4-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[3-4-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[4-None-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[4-None-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[4-None-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[4-None-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[4-2-sort-topk] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-index] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[4-2-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[4-2-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[4-2-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[4-3-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[4-3-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[4-3-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[4-3-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[4-4-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[4-4-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[4-4-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[4-4-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[5-None-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[5-None-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[5-None-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[5-None-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[5-2-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[5-2-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[5-2-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[5-2-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[5-3-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[5-3-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[5-3-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[5-3-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[5-4-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[5-4-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[5-4-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[5-4-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[10-None-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[10-None-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[10-None-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[10-None-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[10-2-sort-topk] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-0] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[10-2-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[10-2-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[10-2-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[10-3-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[10-3-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[10-3-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[10-3-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[10-4-sort-topk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[10-4-sort-topk] 
dask/array/tests/test_reductions.py::test_topk_argtopk2[10-4-argsort-argtopk] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk2[10-4-argsort-argtopk] 
dask/array/tests/test_reductions.py::test_topk_argtopk3 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_topk_argtopk3 
dask/array/tests/test_reductions.py::test_regres_3940[sequential-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[sequential-cumsum] 
dask/array/tests/test_reductions.py::test_regres_3940[sequential-cumprod] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-columns] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[sequential-cumprod] 
dask/array/tests/test_reductions.py::test_regres_3940[sequential-argmin] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[sequential-argmin] 
dask/array/tests/test_reductions.py::test_regres_3940[sequential-argmax] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[sequential-argmax] 
dask/array/tests/test_reductions.py::test_regres_3940[sequential-min] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[sequential-min] 
dask/array/tests/test_reductions.py::test_regres_3940[sequential-max] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[sequential-max] 
dask/array/tests/test_reductions.py::test_regres_3940[sequential-nansum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[sequential-nansum] 
dask/array/tests/test_reductions.py::test_regres_3940[sequential-nanmax] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[sequential-nanmax] 
dask/array/tests/test_reductions.py::test_regres_3940[blelloch-cumsum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[blelloch-cumsum] 
dask/array/tests/test_reductions.py::test_regres_3940[blelloch-cumprod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[blelloch-cumprod] 
dask/array/tests/test_reductions.py::test_regres_3940[blelloch-argmin] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[blelloch-argmin] 
dask/array/tests/test_reductions.py::test_regres_3940[blelloch-argmax] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[blelloch-argmax] 
dask/array/tests/test_reductions.py::test_regres_3940[blelloch-min] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[blelloch-min] 
dask/array/tests/test_reductions.py::test_regres_3940[blelloch-max] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[blelloch-max] 
dask/array/tests/test_reductions.py::test_regres_3940[blelloch-nansum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[blelloch-nansum] 
dask/array/tests/test_reductions.py::test_regres_3940[blelloch-nanmax] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_regres_3940[blelloch-nanmax] 
dask/array/tests/test_reductions.py::test_trace 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_trace 
dask/array/tests/test_reductions.py::test_median[True-0-median] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[True-0-median] 
dask/array/tests/test_reductions.py::test_median[True-0-nanmedian] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[True-0-nanmedian] 
dask/array/tests/test_reductions.py::test_median[True-axis1-median] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-columns] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[True-axis1-median] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-1] 
dask/array/tests/test_reductions.py::test_median[True-axis1-nanmedian] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[True-axis1-nanmedian] 
dask/array/tests/test_reductions.py::test_median[True-1-median] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[True-1-median] 
dask/array/tests/test_reductions.py::test_median[True-1-nanmedian] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[True-1-nanmedian] 
dask/array/tests/test_reductions.py::test_median[True--1-median] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[True--1-median] 
dask/array/tests/test_reductions.py::test_median[True--1-nanmedian] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[True--1-nanmedian] 
dask/array/tests/test_reductions.py::test_median[False-0-median] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[False-0-median] 
dask/array/tests/test_reductions.py::test_median[False-0-nanmedian] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[False-0-nanmedian] 
dask/array/tests/test_reductions.py::test_median[False-axis1-median] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[False-axis1-median] 
dask/array/tests/test_reductions.py::test_median[False-axis1-nanmedian] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[False-axis1-nanmedian] 
dask/array/tests/test_reductions.py::test_median[False-1-median] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[False-1-median] 
dask/array/tests/test_reductions.py::test_median[False-1-nanmedian] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[False-1-nanmedian] 
dask/array/tests/test_reductions.py::test_median[False--1-median] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[False--1-median] 
dask/array/tests/test_reductions.py::test_median[False--1-nanmedian] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median[False--1-nanmedian] 
dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[0-median] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[0-median] 
dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[0-nanmedian] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[0-nanmedian] 
dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[axis1-median] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[axis1-median] 
dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[axis1-nanmedian] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[axis1-nanmedian] 
dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[1-median] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[1-median] 
dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[1-nanmedian] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-index] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_median_does_not_rechunk_if_whole_axis_in_one_chunk[1-nanmedian] 
dask/array/tests/test_reductions.py::test_object_reduction[sum] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_object_reduction[sum] 
dask/array/tests/test_reductions.py::test_object_reduction[mean] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_object_reduction[mean] 
dask/array/tests/test_reductions.py::test_object_reduction[prod] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_object_reduction[prod] 
dask/array/tests/test_reductions.py::test_empty_chunk_nanmin_nanmax[nanmin] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_empty_chunk_nanmin_nanmax[nanmin] 
dask/array/tests/test_reductions.py::test_empty_chunk_nanmin_nanmax[nanmax] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_empty_chunk_nanmin_nanmax[nanmax] 
dask/array/tests/test_reductions.py::test_empty_chunk_nanmin_nanmax_raise[nanmin] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_empty_chunk_nanmin_nanmax_raise[nanmin] 
dask/array/tests/test_reductions.py::test_empty_chunk_nanmin_nanmax_raise[nanmax] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_empty_chunk_nanmin_nanmax_raise[nanmax] 
dask/array/tests/test_reductions.py::test_mean_func_does_not_warn 
[gw1] [ 22%] SKIPPED dask/array/tests/test_reductions.py::test_mean_func_does_not_warn 
dask/array/tests/test_reductions.py::test_nan_func_does_not_warn[nanvar] 
[gw1] [ 22%] PASSED dask/array/tests/test_reductions.py::test_nan_func_does_not_warn[nanvar] 
dask/array/tests/test_reductions.py::test_nan_func_does_not_warn[nanstd] 
[gw0] [ 22%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_nan_func_does_not_warn[nanstd] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes0-4-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks5] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes1-4-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks3] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes2-4-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-index] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks4] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes3-4-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-index] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-index] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes4-4-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks4] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-columns] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes5-4-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks3] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-1] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks2] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks3] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks4] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-1] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-index] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks5] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-0] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes6-4-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks0] 
[gw0] [ 23%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-columns] 
[gw1] [ 23%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks2] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks4] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks5] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks2] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks4] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks5] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes7-4-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks2] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks4] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks5] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks2] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks4] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-columns] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks5] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes8-4-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks2] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks4] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-index] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks5] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks2] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks4] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks5] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes9-4-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks2] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks4] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks5] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks2] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks4] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks5] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes10-4-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks2] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-columns] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks4] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks5] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-2-chunks5] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks0] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks1] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks2] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks2] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks3] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks4] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks4] 
dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks5] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_chunk_structure_independence[axes11-4-chunks5] 
dask/array/tests/test_reductions.py::test_weighted_reduction 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_weighted_reduction 
dask/array/tests/test_reductions.py::test_cumreduction_no_rechunk_on_1d_array 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_cumreduction_no_rechunk_on_1d_array 
dask/array/tests/test_reductions.py::test_nanquantile[True-0.75-3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[True-0.75-3] 
dask/array/tests/test_reductions.py::test_nanquantile[True-0.75-0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-index] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[True-0.75-0] 
dask/array/tests/test_reductions.py::test_nanquantile[True-0.75-axis2] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[True-0.75-axis2] 
dask/array/tests/test_reductions.py::test_nanquantile[True-q1-3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-index] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[True-q1-3] 
dask/array/tests/test_reductions.py::test_nanquantile[True-q1-0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-columns] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[True-q1-0] 
dask/array/tests/test_reductions.py::test_nanquantile[True-q1-axis2] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[True-q1-axis2] 
dask/array/tests/test_reductions.py::test_nanquantile[True-q2-3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[True-q2-3] 
dask/array/tests/test_reductions.py::test_nanquantile[True-q2-0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-index] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[True-q2-0] 
dask/array/tests/test_reductions.py::test_nanquantile[True-q2-axis2] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[True-q2-axis2] 
dask/array/tests/test_reductions.py::test_nanquantile[False-0.75-3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[False-0.75-3] 
dask/array/tests/test_reductions.py::test_nanquantile[False-0.75-0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-index] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[False-0.75-0] 
dask/array/tests/test_reductions.py::test_nanquantile[False-0.75-axis2] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[False-0.75-axis2] 
dask/array/tests/test_reductions.py::test_nanquantile[False-q1-3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-index] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[False-q1-3] 
dask/array/tests/test_reductions.py::test_nanquantile[False-q1-0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-columns] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[False-q1-0] 
dask/array/tests/test_reductions.py::test_nanquantile[False-q1-axis2] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[False-q1-axis2] 
dask/array/tests/test_reductions.py::test_nanquantile[False-q2-3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-columns] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-index] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[False-q2-3] 
dask/array/tests/test_reductions.py::test_nanquantile[False-q2-0] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-columns] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-index] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[False-q2-0] 
dask/array/tests/test_reductions.py::test_nanquantile[False-q2-axis2] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-0] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_nanquantile[False-q2-axis2] 
dask/array/tests/test_reductions.py::test_quantile[True-0.75-3] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_quantile[True-0.75-3] 
dask/array/tests/test_reductions.py::test_quantile[True-0.75-axis1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-columns] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_quantile[True-0.75-axis1] 
dask/array/tests/test_reductions.py::test_quantile[True-q1-3] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-1] 
[gw1] [ 24%] PASSED dask/array/tests/test_reductions.py::test_quantile[True-q1-3] 
dask/array/tests/test_reductions.py::test_quantile[True-q1-axis1] 
[gw0] [ 24%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-index] 
[gw1] [ 25%] PASSED dask/array/tests/test_reductions.py::test_quantile[True-q1-axis1] 
dask/array/tests/test_reductions.py::test_quantile[False-0.75-3] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-0] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-columns] 
[gw1] [ 25%] PASSED dask/array/tests/test_reductions.py::test_quantile[False-0.75-3] 
dask/array/tests/test_reductions.py::test_quantile[False-0.75-axis1] 
[gw1] [ 25%] PASSED dask/array/tests/test_reductions.py::test_quantile[False-0.75-axis1] 
dask/array/tests/test_reductions.py::test_quantile[False-q1-3] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-1] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-index] 
[gw1] [ 25%] PASSED dask/array/tests/test_reductions.py::test_quantile[False-q1-3] 
dask/array/tests/test_reductions.py::test_quantile[False-q1-axis1] 
[gw1] [ 25%] PASSED dask/array/tests/test_reductions.py::test_quantile[False-q1-axis1] 
dask/array/tests/test_reductions.py::test_nanquantile_all_nan 
[gw1] [ 25%] PASSED dask/array/tests/test_reductions.py::test_nanquantile_all_nan 
dask/array/tests/test_reductions.py::test_nanquantile_method 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-0] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-columns] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-1] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-index] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-0] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-columns] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-1] 
[gw1] [ 25%] PASSED dask/array/tests/test_reductions.py::test_nanquantile_method 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-index] 
dask/array/tests/test_reductions.py::test_nanquantile_one_dim 
[gw1] [ 25%] PASSED dask/array/tests/test_reductions.py::test_nanquantile_one_dim 
dask/array/tests/test_reductions.py::test_nanquantile_two_dims 
[gw1] [ 25%] PASSED dask/array/tests/test_reductions.py::test_nanquantile_two_dims 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape0-outshape0-prechunks0-inchunks0-outchunks0] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape0-outshape0-prechunks0-inchunks0-outchunks0] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape1-outshape1-prechunks1-inchunks1-outchunks1] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape1-outshape1-prechunks1-inchunks1-outchunks1] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape2-outshape2-prechunks2-inchunks2-outchunks2] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape2-outshape2-prechunks2-inchunks2-outchunks2] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape3-outshape3-prechunks3-inchunks3-outchunks3] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape3-outshape3-prechunks3-inchunks3-outchunks3] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape4-outshape4-prechunks4-inchunks4-outchunks4] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape4-outshape4-prechunks4-inchunks4-outchunks4] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape5-outshape5-prechunks5-inchunks5-outchunks5] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape5-outshape5-prechunks5-inchunks5-outchunks5] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape6-outshape6-prechunks6-inchunks6-outchunks6] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape6-outshape6-prechunks6-inchunks6-outchunks6] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape7-outshape7-prechunks7-inchunks7-outchunks7] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape7-outshape7-prechunks7-inchunks7-outchunks7] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape8-outshape8-prechunks8-inchunks8-outchunks8] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape8-outshape8-prechunks8-inchunks8-outchunks8] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape9-outshape9-prechunks9-inchunks9-outchunks9] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape9-outshape9-prechunks9-inchunks9-outchunks9] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape10-outshape10-prechunks10-inchunks10-outchunks10] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape10-outshape10-prechunks10-inchunks10-outchunks10] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape11-outshape11-prechunks11-inchunks11-outchunks11] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape11-outshape11-prechunks11-inchunks11-outchunks11] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape12-outshape12-prechunks12-inchunks12-outchunks12] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape12-outshape12-prechunks12-inchunks12-outchunks12] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape13-outshape13-prechunks13-inchunks13-outchunks13] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape13-outshape13-prechunks13-inchunks13-outchunks13] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape14-outshape14-prechunks14-inchunks14-outchunks14] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape14-outshape14-prechunks14-inchunks14-outchunks14] 
dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape15-outshape15-prechunks15-inchunks15-outchunks15] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_rechunk[inshape15-outshape15-prechunks15-inchunks15-outchunks15] 
dask/array/tests/test_reshape.py::test_expand_tuple 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_expand_tuple 
dask/array/tests/test_reshape.py::test_contract_tuple 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_contract_tuple 
dask/array/tests/test_reshape.py::test_reshape_unknown_sizes 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_unknown_sizes 
dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape0-inchunks0-outshape0-outchunks0] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape0-inchunks0-outshape0-outchunks0] 
dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape1-inchunks1-outshape1-outchunks1] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape1-inchunks1-outshape1-outchunks1] 
dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape2-inchunks2-outshape2-outchunks2] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape2-inchunks2-outshape2-outchunks2] 
dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape3-inchunks3-outshape3-outchunks3] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape3-inchunks3-outshape3-outchunks3] 
dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape4-inchunks4-outshape4-outchunks4] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape4-inchunks4-outshape4-outchunks4] 
dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape5-inchunks5-outshape5-outchunks5] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape5-inchunks5-outshape5-outchunks5] 
dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape6-inchunks6-outshape6-outchunks6] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_all_chunked_no_merge[inshape6-inchunks6-outshape6-outchunks6] 
dask/array/tests/test_reshape.py::test_reshape_all_not_chunked_merge[inshape0-inchunks0-expected_inchunks0-outshape0-outchunks0] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_all_not_chunked_merge[inshape0-inchunks0-expected_inchunks0-outshape0-outchunks0] 
dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape0-inchunks0-outshape0-outchunks0] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape0-inchunks0-outshape0-outchunks0] 
dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape1-inchunks1-outshape1-outchunks1] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape1-inchunks1-outshape1-outchunks1] 
dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape2-inchunks2-outshape2-outchunks2] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape2-inchunks2-outshape2-outchunks2] 
dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape3-inchunks3-outshape3-outchunks3] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape3-inchunks3-outshape3-outchunks3] 
dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape4-inchunks4-outshape4-outchunks4] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape4-inchunks4-outshape4-outchunks4] 
dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape5-inchunks5-outshape5-outchunks5] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_merge_chunks[inshape5-inchunks5-outshape5-outchunks5] 
dask/array/tests/test_reshape.py::test_smooth_chunks 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_smooth_chunks 
dask/array/tests/test_reshape.py::test_smooth_chunks_not_divisible 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_smooth_chunks_not_divisible 
dask/array/tests/test_reshape.py::test_smooth_chunks_first_dimension 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_smooth_chunks_first_dimension 
dask/array/tests/test_reshape.py::test_smooth_chunks_large_dimension 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-index] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_smooth_chunks_large_dimension 
dask/array/tests/test_reshape.py::test_reshape_not_squashing_chunks 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-0] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_not_squashing_chunks 
dask/array/tests/test_reshape.py::test_reshape_to_higher_dimension[reshaper0-chunks0] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_to_higher_dimension[reshaper0-chunks0] 
dask/array/tests/test_reshape.py::test_reshape_to_higher_dimension[reshaper1-chunks1] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_to_higher_dimension[reshaper1-chunks1] 
dask/array/tests/test_reshape.py::test_reshape_to_higher_dimension[reshaper2-chunks2] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_to_higher_dimension[reshaper2-chunks2] 
dask/array/tests/test_reshape.py::test_reshape_to_higher_dimension[reshaper3-chunks3] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_to_higher_dimension[reshaper3-chunks3] 
dask/array/tests/test_reshape.py::test_reshape_lower_dimension 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_lower_dimension 
dask/array/tests/test_reshape.py::test_reshape_split_out_chunks 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_split_out_chunks 
dask/array/tests/test_reshape.py::test_reshape_blockwise 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_blockwise 
dask/array/tests/test_reshape.py::test_reshape_blockwise_raises_for_expansion 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_blockwise_raises_for_expansion 
dask/array/tests/test_reshape.py::test_reshape_blockwise_dimension_reduction_and_chunks 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_blockwise_dimension_reduction_and_chunks 
dask/array/tests/test_reshape.py::test_reshape_blockwise_identity 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_reshape_blockwise_identity 
dask/array/tests/test_reshape.py::test_argwhere_reshaping_not_concating_chunks 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-columns] 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_argwhere_reshaping_not_concating_chunks 
dask/array/tests/test_reshape.py::test_blockwise_reshape_sanity_checks 
[gw1] [ 25%] PASSED dask/array/tests/test_reshape.py::test_blockwise_reshape_sanity_checks 
dask/array/tests/test_routines.py::test_array 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_array 
dask/array/tests/test_routines.py::test_array_return_type 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_array_return_type 
dask/array/tests/test_routines.py::test_derived_docstrings 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_derived_docstrings 
dask/array/tests/test_routines.py::test_atleast_nd_no_args[atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_no_args[atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_no_args[atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_no_args[atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_no_args[atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_no_args[atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape0-chunks0-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape0-chunks0-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape0-chunks0-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape0-chunks0-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape0-chunks0-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape0-chunks0-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape1-chunks1-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape1-chunks1-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape1-chunks1-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape1-chunks1-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape1-chunks1-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape1-chunks1-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape2-chunks2-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape2-chunks2-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape2-chunks2-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape2-chunks2-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape2-chunks2-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape2-chunks2-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape3-chunks3-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape3-chunks3-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape3-chunks3-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape3-chunks3-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape3-chunks3-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape3-chunks3-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape4-chunks4-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape4-chunks4-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape4-chunks4-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape4-chunks4-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape4-chunks4-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_one_arg[shape4-chunks4-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape10-shape20-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape10-shape20-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape10-shape20-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape10-shape20-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape10-shape20-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape10-shape20-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape11-shape21-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape11-shape21-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape11-shape21-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape11-shape21-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape11-shape21-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape11-shape21-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape12-shape22-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape12-shape22-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape12-shape22-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape12-shape22-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape12-shape22-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape12-shape22-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape13-shape23-atleast_1d] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-columns] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape13-shape23-atleast_1d] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-1] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape13-shape23-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape13-shape23-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape13-shape23-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape13-shape23-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape14-shape24-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape14-shape24-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape14-shape24-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape14-shape24-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape14-shape24-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape14-shape24-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape15-shape25-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape15-shape25-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape15-shape25-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape15-shape25-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape15-shape25-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape15-shape25-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape16-shape26-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape16-shape26-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape16-shape26-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape16-shape26-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape16-shape26-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape16-shape26-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape17-shape27-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape17-shape27-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape17-shape27-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape17-shape27-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape17-shape27-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape17-shape27-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape18-shape28-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape18-shape28-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape18-shape28-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape18-shape28-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape18-shape28-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape18-shape28-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape19-shape29-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape19-shape29-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape19-shape29-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape19-shape29-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape19-shape29-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape19-shape29-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape110-shape210-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape110-shape210-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape110-shape210-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape110-shape210-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape110-shape210-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape110-shape210-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape111-shape211-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape111-shape211-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape111-shape211-atleast_2d] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-index] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape111-shape211-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape111-shape211-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape111-shape211-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape112-shape212-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape112-shape212-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape112-shape212-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape112-shape212-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape112-shape212-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape112-shape212-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape113-shape213-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape113-shape213-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape113-shape213-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape113-shape213-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape113-shape213-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape113-shape213-atleast_3d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape114-shape214-atleast_1d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape114-shape214-atleast_1d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape114-shape214-atleast_2d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape114-shape214-atleast_2d] 
dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape114-shape214-atleast_3d] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_atleast_nd_two_args[shape114-shape214-atleast_3d] 
dask/array/tests/test_routines.py::test_transpose 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_transpose 
dask/array/tests/test_routines.py::test_transpose_negative_axes 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_transpose_negative_axes 
dask/array/tests/test_routines.py::test_transpose_skip_when_possible 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_transpose_skip_when_possible 
dask/array/tests/test_routines.py::test_swapaxes 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-0] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_swapaxes 
dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape0-moveaxis] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape0-moveaxis] 
dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape0-rollaxis] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape0-rollaxis] 
dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape1-moveaxis] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape1-moveaxis] 
dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape1-rollaxis] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-columns] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape1-rollaxis] 
dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape2-moveaxis] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-1] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-index] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-0] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-columns] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-1] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape2-moveaxis] 
dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape2-rollaxis] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-index] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-0] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-columns] 
[gw0] [ 25%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-1] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_moveaxis_rollaxis[shape2-rollaxis] 
dask/array/tests/test_routines.py::test_moveaxis_rollaxis_keyword 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_moveaxis_rollaxis_keyword 
dask/array/tests/test_routines.py::test_moveaxis_rollaxis_numpy_api 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_moveaxis_rollaxis_numpy_api 
dask/array/tests/test_routines.py::test_flip[shape0-flipud-kwargs0] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape0-flipud-kwargs0] 
dask/array/tests/test_routines.py::test_flip[shape0-fliplr-kwargs1] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape0-fliplr-kwargs1] 
dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs2] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs2] 
dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs3] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs3] 
dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs4] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs4] 
dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs5] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs5] 
dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs6] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs6] 
dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs7] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape0-flip-kwargs7] 
dask/array/tests/test_routines.py::test_flip[shape1-flipud-kwargs0] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape1-flipud-kwargs0] 
dask/array/tests/test_routines.py::test_flip[shape1-fliplr-kwargs1] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape1-fliplr-kwargs1] 
dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs2] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs2] 
dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs3] 
[gw1] [ 25%] PASSED dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs3] 
dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs4] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs4] 
dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs5] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs5] 
dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs6] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs6] 
dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs7] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape1-flip-kwargs7] 
dask/array/tests/test_routines.py::test_flip[shape2-flipud-kwargs0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape2-flipud-kwargs0] 
dask/array/tests/test_routines.py::test_flip[shape2-fliplr-kwargs1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape2-fliplr-kwargs1] 
dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs2] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs2] 
dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs3] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs3] 
dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs4] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs4] 
dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs5] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs5] 
dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs6] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-index] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs6] 
dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs7] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape2-flip-kwargs7] 
dask/array/tests/test_routines.py::test_flip[shape3-flipud-kwargs0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape3-flipud-kwargs0] 
dask/array/tests/test_routines.py::test_flip[shape3-fliplr-kwargs1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape3-fliplr-kwargs1] 
dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs2] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs2] 
dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs3] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs3] 
dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs4] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs4] 
dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs5] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs5] 
dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs6] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs6] 
dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs7] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-columns] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape3-flip-kwargs7] 
dask/array/tests/test_routines.py::test_flip[shape4-flipud-kwargs0] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-1] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-index] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape4-flipud-kwargs0] 
dask/array/tests/test_routines.py::test_flip[shape4-fliplr-kwargs1] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-columns] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-1] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-index] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape4-fliplr-kwargs1] 
dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs2] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-0] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-columns] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs2] 
dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs3] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-index] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-0] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-columns] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs3] 
dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs4] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-index] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-0] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-columns] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs4] 
dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs5] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-1] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-index] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-0] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-columns] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs5] 
dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs6] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-1] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-index] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs6] 
dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs7] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-columns] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-1] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-index] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_flip[shape4-flip-kwargs7] 
dask/array/tests/test_routines.py::test_rot90[shape0-kwargs0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape0-kwargs0] 
dask/array/tests/test_routines.py::test_rot90[shape0-kwargs1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape0-kwargs1] 
dask/array/tests/test_routines.py::test_rot90[shape0-kwargs2] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape0-kwargs2] 
dask/array/tests/test_routines.py::test_rot90[shape0-kwargs3] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape0-kwargs3] 
dask/array/tests/test_routines.py::test_rot90[shape1-kwargs0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape1-kwargs0] 
dask/array/tests/test_routines.py::test_rot90[shape1-kwargs1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape1-kwargs1] 
dask/array/tests/test_routines.py::test_rot90[shape1-kwargs2] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape1-kwargs2] 
dask/array/tests/test_routines.py::test_rot90[shape1-kwargs3] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape1-kwargs3] 
dask/array/tests/test_routines.py::test_rot90[shape2-kwargs0] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape2-kwargs0] 
dask/array/tests/test_routines.py::test_rot90[shape2-kwargs1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape2-kwargs1] 
dask/array/tests/test_routines.py::test_rot90[shape2-kwargs2] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape2-kwargs2] 
dask/array/tests/test_routines.py::test_rot90[shape2-kwargs3] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape2-kwargs3] 
dask/array/tests/test_routines.py::test_rot90[shape3-kwargs0] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-columns] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape3-kwargs0] 
dask/array/tests/test_routines.py::test_rot90[shape3-kwargs1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape3-kwargs1] 
dask/array/tests/test_routines.py::test_rot90[shape3-kwargs2] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape3-kwargs2] 
dask/array/tests/test_routines.py::test_rot90[shape3-kwargs3] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_rot90[shape3-kwargs3] 
dask/array/tests/test_routines.py::test_matmul[x_shape0-y_shape0-x_chunks0-y_chunks0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape0-y_shape0-x_chunks0-y_chunks0] 
dask/array/tests/test_routines.py::test_matmul[x_shape1-y_shape1-x_chunks1-y_chunks1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape1-y_shape1-x_chunks1-y_chunks1] 
dask/array/tests/test_routines.py::test_matmul[x_shape2-y_shape2-x_chunks2-y_chunks2] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape2-y_shape2-x_chunks2-y_chunks2] 
dask/array/tests/test_routines.py::test_matmul[x_shape3-y_shape3-x_chunks3-y_chunks3] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape3-y_shape3-x_chunks3-y_chunks3] 
dask/array/tests/test_routines.py::test_matmul[x_shape4-y_shape4-x_chunks4-y_chunks4] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape4-y_shape4-x_chunks4-y_chunks4] 
dask/array/tests/test_routines.py::test_matmul[x_shape5-y_shape5-x_chunks5-y_chunks5] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape5-y_shape5-x_chunks5-y_chunks5] 
dask/array/tests/test_routines.py::test_matmul[x_shape6-y_shape6-x_chunks6-y_chunks6] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape6-y_shape6-x_chunks6-y_chunks6] 
dask/array/tests/test_routines.py::test_matmul[x_shape7-y_shape7-x_chunks7-y_chunks7] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape7-y_shape7-x_chunks7-y_chunks7] 
dask/array/tests/test_routines.py::test_matmul[x_shape8-y_shape8-x_chunks8-y_chunks8] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape8-y_shape8-x_chunks8-y_chunks8] 
dask/array/tests/test_routines.py::test_matmul[x_shape9-y_shape9-x_chunks9-y_chunks9] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-index] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape9-y_shape9-x_chunks9-y_chunks9] 
dask/array/tests/test_routines.py::test_matmul[x_shape10-y_shape10-x_chunks10-y_chunks10] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape10-y_shape10-x_chunks10-y_chunks10] 
dask/array/tests/test_routines.py::test_matmul[x_shape11-y_shape11-x_chunks11-y_chunks11] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape11-y_shape11-x_chunks11-y_chunks11] 
dask/array/tests/test_routines.py::test_matmul[x_shape12-y_shape12-x_chunks12-y_chunks12] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape12-y_shape12-x_chunks12-y_chunks12] 
dask/array/tests/test_routines.py::test_matmul[x_shape13-y_shape13-x_chunks13-y_chunks13] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape13-y_shape13-x_chunks13-y_chunks13] 
dask/array/tests/test_routines.py::test_matmul[x_shape14-y_shape14-x_chunks14-y_chunks14] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape14-y_shape14-x_chunks14-y_chunks14] 
dask/array/tests/test_routines.py::test_matmul[x_shape15-y_shape15-x_chunks15-y_chunks15] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape15-y_shape15-x_chunks15-y_chunks15] 
dask/array/tests/test_routines.py::test_matmul[x_shape16-y_shape16-x_chunks16-y_chunks16] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-columns] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape16-y_shape16-x_chunks16-y_chunks16] 
dask/array/tests/test_routines.py::test_matmul[x_shape17-y_shape17-x_chunks17-y_chunks17] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape17-y_shape17-x_chunks17-y_chunks17] 
dask/array/tests/test_routines.py::test_matmul[x_shape18-y_shape18-x_chunks18-y_chunks18] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape18-y_shape18-x_chunks18-y_chunks18] 
dask/array/tests/test_routines.py::test_matmul[x_shape19-y_shape19-x_chunks19-y_chunks19] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-index] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape19-y_shape19-x_chunks19-y_chunks19] 
dask/array/tests/test_routines.py::test_matmul[x_shape20-y_shape20-x_chunks20-y_chunks20] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape20-y_shape20-x_chunks20-y_chunks20] 
dask/array/tests/test_routines.py::test_matmul[x_shape21-y_shape21-x_chunks21-y_chunks21] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape21-y_shape21-x_chunks21-y_chunks21] 
dask/array/tests/test_routines.py::test_matmul[x_shape22-y_shape22-x_chunks22-y_chunks22] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape22-y_shape22-x_chunks22-y_chunks22] 
dask/array/tests/test_routines.py::test_matmul[x_shape23-y_shape23-x_chunks23-y_chunks23] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-columns] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape23-y_shape23-x_chunks23-y_chunks23] 
dask/array/tests/test_routines.py::test_matmul[x_shape24-y_shape24-x_chunks24-y_chunks24] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape24-y_shape24-x_chunks24-y_chunks24] 
dask/array/tests/test_routines.py::test_matmul[x_shape25-y_shape25-x_chunks25-y_chunks25] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape25-y_shape25-x_chunks25-y_chunks25] 
dask/array/tests/test_routines.py::test_matmul[x_shape26-y_shape26-x_chunks26-y_chunks26] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape26-y_shape26-x_chunks26-y_chunks26] 
dask/array/tests/test_routines.py::test_matmul[x_shape27-y_shape27-x_chunks27-y_chunks27] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-index] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape27-y_shape27-x_chunks27-y_chunks27] 
dask/array/tests/test_routines.py::test_matmul[x_shape28-y_shape28-x_chunks28-y_chunks28] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape28-y_shape28-x_chunks28-y_chunks28] 
dask/array/tests/test_routines.py::test_matmul[x_shape29-y_shape29-x_chunks29-y_chunks29] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape29-y_shape29-x_chunks29-y_chunks29] 
dask/array/tests/test_routines.py::test_matmul[x_shape30-y_shape30-x_chunks30-y_chunks30] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-columns] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape30-y_shape30-x_chunks30-y_chunks30] 
dask/array/tests/test_routines.py::test_matmul[x_shape31-y_shape31-x_chunks31-y_chunks31] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-index] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_matmul[x_shape31-y_shape31-x_chunks31-y_chunks31] 
dask/array/tests/test_routines.py::test_tensordot 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-columns] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot 
dask/array/tests/test_routines.py::test_tensordot_2[0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_2[0] 
dask/array/tests/test_routines.py::test_tensordot_2[1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_2[1] 
dask/array/tests/test_routines.py::test_tensordot_2[axes2] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_2[axes2] 
dask/array/tests/test_routines.py::test_tensordot_2[axes3] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_2[axes3] 
dask/array/tests/test_routines.py::test_tensordot_2[axes4] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_2[axes4] 
dask/array/tests/test_routines.py::test_tensordot_2[axes5] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_2[axes5] 
dask/array/tests/test_routines.py::test_tensordot_2[axes6] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_2[axes6] 
dask/array/tests/test_routines.py::test_tensordot_double_contraction_neq2[auto] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_double_contraction_neq2[auto] 
dask/array/tests/test_routines.py::test_tensordot_double_contraction_neq2[chunks1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_double_contraction_neq2[chunks1] 
dask/array/tests/test_routines.py::test_tensordot_double_contraction_neq2[chunks2] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_double_contraction_neq2[chunks2] 
dask/array/tests/test_routines.py::test_tensordot_double_contraction_neq2[chunks3] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_double_contraction_neq2[chunks3] 
dask/array/tests/test_routines.py::test_tensordot_double_contraction_neq2[chunks4] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_double_contraction_neq2[chunks4] 
dask/array/tests/test_routines.py::test_tensordot_double_contraction_ngt2 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_double_contraction_ngt2 
dask/array/tests/test_routines.py::test_tensordot_more_than_26_dims 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-index] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-0] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-columns] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-1] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-index] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-0] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-columns] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-1] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-index] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-0] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-columns] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_tensordot_more_than_26_dims 
dask/array/tests/test_routines.py::test_dot_method 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_dot_method 
dask/array/tests/test_routines.py::test_dot_persist_equivalence 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_dot_persist_equivalence 
dask/array/tests/test_routines.py::test_vdot[shape0-chunks0] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-index] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_vdot[shape0-chunks0] 
dask/array/tests/test_routines.py::test_vdot[shape1-chunks1] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_vdot[shape1-chunks1] 
dask/array/tests/test_routines.py::test_outer[shape10-shape20] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_outer[shape10-shape20] 
dask/array/tests/test_routines.py::test_outer[shape11-shape21] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_outer[shape11-shape21] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape0-0-ndim-<lambda>-False] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape0-0-ndim-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape0-0-sum-<lambda>-False] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape0-0-sum-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape0-0-range-<lambda>-False] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape0-0-range-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape0-0-range2-<lambda>-False] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-0] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape0-0-range2-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape0-0-cumsum-<lambda>-True] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape0-0-cumsum-<lambda>-True] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape1-1-ndim-<lambda>-False] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape1-1-ndim-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape1-1-sum-<lambda>-False] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape1-1-sum-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape1-1-range-<lambda>-False] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape1-1-range-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape1-1-range2-<lambda>-False] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape1-1-range2-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape1-1-cumsum-<lambda>-True] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape1-1-cumsum-<lambda>-True] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape2-2-ndim-<lambda>-False] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape2-2-ndim-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape2-2-sum-<lambda>-False] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape2-2-sum-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape2-2-range-<lambda>-False] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape2-2-range-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape2-2-range2-<lambda>-False] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape2-2-range2-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape2-2-cumsum-<lambda>-True] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape2-2-cumsum-<lambda>-True] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape3--1-ndim-<lambda>-False] 
[gw0] [ 26%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-columns] 
[gw1] [ 26%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape3--1-ndim-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape3--1-sum-<lambda>-False] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape3--1-sum-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape3--1-range-<lambda>-False] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape3--1-range-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape3--1-range2-<lambda>-False] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape3--1-range2-<lambda>-False] 
dask/array/tests/test_routines.py::test_apply_along_axis[input_shape3--1-cumsum-<lambda>-True] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_along_axis[input_shape3--1-cumsum-<lambda>-True] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape0-axes0-sum0-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape0-axes0-sum0-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape0-axes0-sum1-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape0-axes0-sum1-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape0-axes0-range-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape0-axes0-range-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape1-0-sum0-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape1-0-sum0-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape1-0-sum1-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape1-0-sum1-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape1-0-range-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape1-0-range-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape2-axes2-sum0-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape2-axes2-sum0-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape2-axes2-sum1-<lambda>] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape2-axes2-sum1-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape2-axes2-range-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape2-axes2-range-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape3-axes3-sum0-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape3-axes3-sum0-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape3-axes3-sum1-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape3-axes3-sum1-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape3-axes3-range-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape3-axes3-range-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape4-axes4-sum0-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape4-axes4-sum0-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape4-axes4-sum1-<lambda>] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-index] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape4-axes4-sum1-<lambda>] 
dask/array/tests/test_routines.py::test_apply_over_axes[shape4-axes4-range-<lambda>] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_apply_over_axes[shape4-axes4-range-<lambda>] 
dask/array/tests/test_routines.py::test_ptp[shape0-None] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_ptp[shape0-None] 
dask/array/tests/test_routines.py::test_ptp[shape1-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_ptp[shape1-0] 
dask/array/tests/test_routines.py::test_ptp[shape2-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_ptp[shape2-1] 
dask/array/tests/test_routines.py::test_ptp[shape3-2] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-index] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_ptp[shape3-2] 
dask/array/tests/test_routines.py::test_ptp[shape4--1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_ptp[shape4--1] 
dask/array/tests/test_routines.py::test_diff[0-shape0-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[0-shape0-0] 
dask/array/tests/test_routines.py::test_diff[0-shape1-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[0-shape1-1] 
dask/array/tests/test_routines.py::test_diff[0-shape2-2] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[0-shape2-2] 
dask/array/tests/test_routines.py::test_diff[0-shape3--1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[0-shape3--1] 
dask/array/tests/test_routines.py::test_diff[1-shape0-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[1-shape0-0] 
dask/array/tests/test_routines.py::test_diff[1-shape1-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[1-shape1-1] 
dask/array/tests/test_routines.py::test_diff[1-shape2-2] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[1-shape2-2] 
dask/array/tests/test_routines.py::test_diff[1-shape3--1] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-columns] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[1-shape3--1] 
dask/array/tests/test_routines.py::test_diff[2-shape0-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[2-shape0-0] 
dask/array/tests/test_routines.py::test_diff[2-shape1-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[2-shape1-1] 
dask/array/tests/test_routines.py::test_diff[2-shape2-2] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[2-shape2-2] 
dask/array/tests/test_routines.py::test_diff[2-shape3--1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff[2-shape3--1] 
dask/array/tests/test_routines.py::test_diff_prepend[0] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-index] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff_prepend[0] 
dask/array/tests/test_routines.py::test_diff_prepend[1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff_prepend[1] 
dask/array/tests/test_routines.py::test_diff_prepend[2] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff_prepend[2] 
dask/array/tests/test_routines.py::test_diff_append[0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff_append[0] 
dask/array/tests/test_routines.py::test_diff_append[1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff_append[1] 
dask/array/tests/test_routines.py::test_diff_append[2] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-columns] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff_append[2] 
dask/array/tests/test_routines.py::test_diff_negative_order 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_diff_negative_order 
dask/array/tests/test_routines.py::test_ediff1d[None-None-shape0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_ediff1d[None-None-shape0] 
dask/array/tests/test_routines.py::test_ediff1d[None-None-shape1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_ediff1d[None-None-shape1] 
dask/array/tests/test_routines.py::test_ediff1d[0-0-shape0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_ediff1d[0-0-shape0] 
dask/array/tests/test_routines.py::test_ediff1d[0-0-shape1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_ediff1d[0-0-shape1] 
dask/array/tests/test_routines.py::test_ediff1d[to_end2-to_begin2-shape0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_ediff1d[to_end2-to_begin2-shape0] 
dask/array/tests/test_routines.py::test_ediff1d[to_end2-to_begin2-shape1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_ediff1d[to_end2-to_begin2-shape1] 
dask/array/tests/test_routines.py::test_gradient[1-shape0-varargs0-None] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[1-shape0-varargs0-None] 
dask/array/tests/test_routines.py::test_gradient[1-shape1-varargs1-None] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-index] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[1-shape1-varargs1-None] 
dask/array/tests/test_routines.py::test_gradient[1-shape2-varargs2-None] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[1-shape2-varargs2-None] 
dask/array/tests/test_routines.py::test_gradient[1-shape3-varargs3-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[1-shape3-varargs3-0] 
dask/array/tests/test_routines.py::test_gradient[1-shape4-varargs4-1] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-columns] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[1-shape4-varargs4-1] 
dask/array/tests/test_routines.py::test_gradient[1-shape5-varargs5-2] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[1-shape5-varargs5-2] 
dask/array/tests/test_routines.py::test_gradient[1-shape6-varargs6--1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[1-shape6-varargs6--1] 
dask/array/tests/test_routines.py::test_gradient[1-shape7-varargs7-axis7] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-1] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-index] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[1-shape7-varargs7-axis7] 
dask/array/tests/test_routines.py::test_gradient[1-shape8-varargs8-axis8] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[1-shape8-varargs8-axis8] 
dask/array/tests/test_routines.py::test_gradient[1-shape9-varargs9-axis9] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-columns] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[1-shape9-varargs9-axis9] 
dask/array/tests/test_routines.py::test_gradient[1-shape10-varargs10--1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[1-shape10-varargs10--1] 
dask/array/tests/test_routines.py::test_gradient[2-shape0-varargs0-None] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[2-shape0-varargs0-None] 
dask/array/tests/test_routines.py::test_gradient[2-shape1-varargs1-None] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-index] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[2-shape1-varargs1-None] 
dask/array/tests/test_routines.py::test_gradient[2-shape2-varargs2-None] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-0] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-columns] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[2-shape2-varargs2-None] 
dask/array/tests/test_routines.py::test_gradient[2-shape3-varargs3-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[2-shape3-varargs3-0] 
dask/array/tests/test_routines.py::test_gradient[2-shape4-varargs4-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[2-shape4-varargs4-1] 
dask/array/tests/test_routines.py::test_gradient[2-shape5-varargs5-2] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[2-shape5-varargs5-2] 
dask/array/tests/test_routines.py::test_gradient[2-shape6-varargs6--1] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[2-shape6-varargs6--1] 
dask/array/tests/test_routines.py::test_gradient[2-shape7-varargs7-axis7] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[2-shape7-varargs7-axis7] 
dask/array/tests/test_routines.py::test_gradient[2-shape8-varargs8-axis8] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-index] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[2-shape8-varargs8-axis8] 
dask/array/tests/test_routines.py::test_gradient[2-shape9-varargs9-axis9] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[2-shape9-varargs9-axis9] 
dask/array/tests/test_routines.py::test_gradient[2-shape10-varargs10--1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_gradient[2-shape10-varargs10--1] 
dask/array/tests/test_routines.py::test_bincount 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-columns] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_bincount 
dask/array/tests/test_routines.py::test_bincount_with_weights[weights0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_bincount_with_weights[weights0] 
dask/array/tests/test_routines.py::test_bincount_with_weights[weights1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_bincount_with_weights[weights1] 
dask/array/tests/test_routines.py::test_bincount_unspecified_minlength 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_bincount_unspecified_minlength 
dask/array/tests/test_routines.py::test_digitize 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_digitize 
dask/array/tests/test_routines.py::test_searchsorted[left-a0-1-v0-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[left-a0-1-v0-1] 
dask/array/tests/test_routines.py::test_searchsorted[left-a1-1-v1-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[left-a1-1-v1-1] 
dask/array/tests/test_routines.py::test_searchsorted[left-a2-3-v2-2] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[left-a2-3-v2-2] 
dask/array/tests/test_routines.py::test_searchsorted[left-a3-3-v3-5] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[left-a3-3-v3-5] 
dask/array/tests/test_routines.py::test_searchsorted[left-a4-3-v4-5] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[left-a4-3-v4-5] 
dask/array/tests/test_routines.py::test_searchsorted[left-a5-3-v5-v_chunks5] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[left-a5-3-v5-v_chunks5] 
dask/array/tests/test_routines.py::test_searchsorted[right-a0-1-v0-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[right-a0-1-v0-1] 
dask/array/tests/test_routines.py::test_searchsorted[right-a1-1-v1-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[right-a1-1-v1-1] 
dask/array/tests/test_routines.py::test_searchsorted[right-a2-3-v2-2] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[right-a2-3-v2-2] 
dask/array/tests/test_routines.py::test_searchsorted[right-a3-3-v3-5] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-index] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[right-a3-3-v3-5] 
dask/array/tests/test_routines.py::test_searchsorted[right-a4-3-v4-5] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[right-a4-3-v4-5] 
dask/array/tests/test_routines.py::test_searchsorted[right-a5-3-v5-v_chunks5] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted[right-a5-3-v5-v_chunks5] 
dask/array/tests/test_routines.py::test_searchsorted_sorter_not_implemented 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_searchsorted_sorter_not_implemented 
dask/array/tests/test_routines.py::test_histogram 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram 
dask/array/tests/test_routines.py::test_histogram_alternative_bins_range 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_alternative_bins_range 
dask/array/tests/test_routines.py::test_histogram_bins_range_with_nan_array 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bins_range_with_nan_array 
dask/array/tests/test_routines.py::test_histogram_return_type 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_return_type 
dask/array/tests/test_routines.py::test_histogram_extra_args_and_shapes 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-0] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-columns] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_extra_args_and_shapes 
dask/array/tests/test_routines.py::test_histogram_normed_deprecation 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_normed_deprecation 
dask/array/tests/test_routines.py::test_histogram_bin_range_raises[None-None] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bin_range_raises[None-None] 
dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-None] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-None] 
dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-1] 
dask/array/tests/test_routines.py::test_histogram_bin_range_raises[None-hist_range3] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bin_range_raises[None-hist_range3] 
dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-hist_range4] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-hist_range4] 
dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-hist_range5] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-hist_range5] 
dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-hist_range6] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-hist_range6] 
dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-hist_range7] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bin_range_raises[10-hist_range7] 
dask/array/tests/test_routines.py::test_histogram_bin_range_raises[bins8-None] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bin_range_raises[bins8-None] 
dask/array/tests/test_routines.py::test_histogram_bin_range_raises[bins9-None] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bin_range_raises[bins9-None] 
dask/array/tests/test_routines.py::test_histogram_bin_range_raises[bins10-None] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_bin_range_raises[bins10-None] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[False-None-True-True] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[False-None-True-True] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[False-None-True-False] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-index] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[False-None-True-False] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[False-None-False-True] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[False-None-False-True] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[False-None-False-False] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[False-None-False-False] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[False-0-True-True] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[False-0-True-True] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[False-0-True-False] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-columns] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[False-0-True-False] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[False-0-False-True] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[False-0-False-True] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[False-0-False-False] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[False-0-False-False] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[True-None-True-True] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[True-None-True-True] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[True-None-True-False] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-index] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[True-None-True-False] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[True-None-False-True] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[True-None-False-True] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[True-None-False-False] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[True-None-False-False] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[True-0-True-True] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-columns] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[True-0-True-True] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[True-0-True-False] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[True-0-True-False] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[True-0-False-True] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-1] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[True-0-False-True] 
dask/array/tests/test_routines.py::test_histogram_delayed_range[True-0-False-False] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_range[True-0-False-False] 
dask/array/tests/test_routines.py::test_histogram_delayed_bins[True-True] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_bins[True-True] 
dask/array/tests/test_routines.py::test_histogram_delayed_bins[True-False] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-index] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_bins[True-False] 
dask/array/tests/test_routines.py::test_histogram_delayed_bins[False-True] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_bins[False-True] 
dask/array/tests/test_routines.py::test_histogram_delayed_bins[False-False] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_bins[False-False] 
dask/array/tests/test_routines.py::test_histogram_delayed_n_bins_raises_with_density 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram_delayed_n_bins_raises_with_density 
dask/array/tests/test_routines.py::test_histogram2d[bins0-True-True] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram2d[bins0-True-True] 
dask/array/tests/test_routines.py::test_histogram2d[bins0-True-False] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram2d[bins0-True-False] 
dask/array/tests/test_routines.py::test_histogram2d[bins0-False-True] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-0] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram2d[bins0-False-True] 
dask/array/tests/test_routines.py::test_histogram2d[bins0-False-False] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram2d[bins0-False-False] 
dask/array/tests/test_routines.py::test_histogram2d[5-True-True] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram2d[5-True-True] 
dask/array/tests/test_routines.py::test_histogram2d[5-True-False] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-columns] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram2d[5-True-False] 
dask/array/tests/test_routines.py::test_histogram2d[5-False-True] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram2d[5-False-True] 
dask/array/tests/test_routines.py::test_histogram2d[5-False-False] 
[gw1] [ 27%] PASSED dask/array/tests/test_routines.py::test_histogram2d[5-False-False] 
dask/array/tests/test_routines.py::test_histogram2d_array_bins[True-True] 
[gw0] [ 27%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogram2d_array_bins[True-True] 
dask/array/tests/test_routines.py::test_histogram2d_array_bins[True-False] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogram2d_array_bins[True-False] 
dask/array/tests/test_routines.py::test_histogram2d_array_bins[False-True] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogram2d_array_bins[False-True] 
dask/array/tests/test_routines.py::test_histogram2d_array_bins[False-False] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-index] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogram2d_array_bins[False-False] 
dask/array/tests/test_routines.py::test_histogramdd 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd 
dask/array/tests/test_routines.py::test_histogramdd_seq_of_arrays 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_seq_of_arrays 
dask/array/tests/test_routines.py::test_histogramdd_alternative_bins_range 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_alternative_bins_range 
dask/array/tests/test_routines.py::test_histogramdd_weighted 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_weighted 
dask/array/tests/test_routines.py::test_histogramdd_density 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-columns] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_density 
dask/array/tests/test_routines.py::test_histogramdd_weighted_density 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_weighted_density 
dask/array/tests/test_routines.py::test_histogramdd_raises_incompat_sample_chunks 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_raises_incompat_sample_chunks 
dask/array/tests/test_routines.py::test_histogramdd_raises_incompat_multiarg_chunks 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_raises_incompat_multiarg_chunks 
dask/array/tests/test_routines.py::test_histogramdd_raises_incompat_weight_chunks 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_raises_incompat_weight_chunks 
dask/array/tests/test_routines.py::test_histogramdd_raises_incompat_bins_or_range 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_raises_incompat_bins_or_range 
dask/array/tests/test_routines.py::test_histogramdd_raise_normed_and_density 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_raise_normed_and_density 
dask/array/tests/test_routines.py::test_histogramdd_raise_incompat_shape 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_raise_incompat_shape 
dask/array/tests/test_routines.py::test_histogramdd_edges 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_histogramdd_edges 
dask/array/tests/test_routines.py::test_cov 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_cov 
dask/array/tests/test_routines.py::test_corrcoef 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_corrcoef 
dask/array/tests/test_routines.py::test_round 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_round 
dask/array/tests/test_routines.py::test_unique_kwargs[False-False-False] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-index] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_kwargs[False-False-False] 
dask/array/tests/test_routines.py::test_unique_kwargs[False-False-True] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_kwargs[False-False-True] 
dask/array/tests/test_routines.py::test_unique_kwargs[False-True-False] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_kwargs[False-True-False] 
dask/array/tests/test_routines.py::test_unique_kwargs[False-True-True] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_kwargs[False-True-True] 
dask/array/tests/test_routines.py::test_unique_kwargs[True-False-False] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_kwargs[True-False-False] 
dask/array/tests/test_routines.py::test_unique_kwargs[True-False-True] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_kwargs[True-False-True] 
dask/array/tests/test_routines.py::test_unique_kwargs[True-True-False] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_kwargs[True-True-False] 
dask/array/tests/test_routines.py::test_unique_kwargs[True-True-True] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_kwargs[True-True-True] 
dask/array/tests/test_routines.py::test_unique_rand[shape0-chunks0-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_rand[shape0-chunks0-23] 
dask/array/tests/test_routines.py::test_unique_rand[shape0-chunks0-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_rand[shape0-chunks0-796] 
dask/array/tests/test_routines.py::test_unique_rand[shape1-chunks1-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_rand[shape1-chunks1-23] 
dask/array/tests/test_routines.py::test_unique_rand[shape1-chunks1-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_rand[shape1-chunks1-796] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-columns] 
dask/array/tests/test_routines.py::test_unique_rand[shape2-chunks2-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_rand[shape2-chunks2-23] 
dask/array/tests/test_routines.py::test_unique_rand[shape2-chunks2-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_rand[shape2-chunks2-796] 
dask/array/tests/test_routines.py::test_unique_rand[shape3-chunks3-23] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_rand[shape3-chunks3-23] 
dask/array/tests/test_routines.py::test_unique_rand[shape3-chunks3-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-index] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_unique_rand[shape3-chunks3-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape0-elements_chunks0-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape0-elements_chunks0-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape0-elements_chunks0-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape0-elements_chunks0-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape1-elements_chunks1-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape1-elements_chunks1-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape1-elements_chunks1-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape1-elements_chunks1-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape2-elements_chunks2-0-10-23] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape2-elements_chunks2-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape2-elements_chunks2-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape2-elements_chunks2-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape3-elements_chunks3-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape3-elements_chunks3-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape3-elements_chunks3-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape0-test_chunks0-elements_shape3-elements_chunks3-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape0-elements_chunks0-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape0-elements_chunks0-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape0-elements_chunks0-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape0-elements_chunks0-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape1-elements_chunks1-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape1-elements_chunks1-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape1-elements_chunks1-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape1-elements_chunks1-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape2-elements_chunks2-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape2-elements_chunks2-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape2-elements_chunks2-0-10-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-columns] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape2-elements_chunks2-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape3-elements_chunks3-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape3-elements_chunks3-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape3-elements_chunks3-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape1-test_chunks1-elements_shape3-elements_chunks3-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape0-elements_chunks0-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape0-elements_chunks0-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape0-elements_chunks0-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape0-elements_chunks0-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape1-elements_chunks1-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape1-elements_chunks1-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape1-elements_chunks1-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape1-elements_chunks1-0-10-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-1] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape2-elements_chunks2-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape2-elements_chunks2-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape2-elements_chunks2-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape2-elements_chunks2-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape3-elements_chunks3-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape3-elements_chunks3-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape3-elements_chunks3-0-10-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-index] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape2-test_chunks2-elements_shape3-elements_chunks3-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape0-elements_chunks0-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape0-elements_chunks0-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape0-elements_chunks0-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape0-elements_chunks0-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape1-elements_chunks1-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape1-elements_chunks1-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape1-elements_chunks1-0-10-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape1-elements_chunks1-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape2-elements_chunks2-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape2-elements_chunks2-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape2-elements_chunks2-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape2-elements_chunks2-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape3-elements_chunks3-0-10-23] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-columns] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape3-elements_chunks3-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape3-elements_chunks3-0-10-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-index] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[True-test_shape3-test_chunks3-elements_shape3-elements_chunks3-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape0-elements_chunks0-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape0-elements_chunks0-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape0-elements_chunks0-0-10-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-index] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape0-elements_chunks0-0-10-796] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-0] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape1-elements_chunks1-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape1-elements_chunks1-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape1-elements_chunks1-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape1-elements_chunks1-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape2-elements_chunks2-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape2-elements_chunks2-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape2-elements_chunks2-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape2-elements_chunks2-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape3-elements_chunks3-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape3-elements_chunks3-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape3-elements_chunks3-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape0-test_chunks0-elements_shape3-elements_chunks3-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape0-elements_chunks0-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape0-elements_chunks0-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape0-elements_chunks0-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape0-elements_chunks0-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape1-elements_chunks1-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape1-elements_chunks1-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape1-elements_chunks1-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape1-elements_chunks1-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape2-elements_chunks2-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape2-elements_chunks2-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape2-elements_chunks2-0-10-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-columns] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape2-elements_chunks2-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape3-elements_chunks3-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape3-elements_chunks3-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape3-elements_chunks3-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape1-test_chunks1-elements_shape3-elements_chunks3-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape0-elements_chunks0-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape0-elements_chunks0-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape0-elements_chunks0-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape0-elements_chunks0-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape1-elements_chunks1-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape1-elements_chunks1-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape1-elements_chunks1-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape1-elements_chunks1-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape2-elements_chunks2-0-10-23] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape2-elements_chunks2-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape2-elements_chunks2-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape2-elements_chunks2-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape3-elements_chunks3-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape3-elements_chunks3-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape3-elements_chunks3-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape2-test_chunks2-elements_shape3-elements_chunks3-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape0-elements_chunks0-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape0-elements_chunks0-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape0-elements_chunks0-0-10-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-index] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape0-elements_chunks0-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape1-elements_chunks1-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape1-elements_chunks1-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape1-elements_chunks1-0-10-796] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape1-elements_chunks1-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape2-elements_chunks2-0-10-23] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape2-elements_chunks2-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape2-elements_chunks2-0-10-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape2-elements_chunks2-0-10-796] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape3-elements_chunks3-0-10-23] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-columns] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape3-elements_chunks3-0-10-23] 
dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape3-elements_chunks3-0-10-796] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-1] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-index] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_rand[False-test_shape3-test_chunks3-elements_shape3-elements_chunks3-0-10-796] 
dask/array/tests/test_routines.py::test_isin_assume_unique[True] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_assume_unique[True] 
dask/array/tests/test_routines.py::test_isin_assume_unique[False] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_isin_assume_unique[False] 
dask/array/tests/test_routines.py::test_roll[None-3-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[None-3-chunks0] 
dask/array/tests/test_routines.py::test_roll[None-3-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[None-3-chunks1] 
dask/array/tests/test_routines.py::test_roll[None-7-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[None-7-chunks0] 
dask/array/tests/test_routines.py::test_roll[None-7-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[None-7-chunks1] 
dask/array/tests/test_routines.py::test_roll[None-9-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[None-9-chunks0] 
dask/array/tests/test_routines.py::test_roll[None-9-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[None-9-chunks1] 
dask/array/tests/test_routines.py::test_roll[None-shift3-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[None-shift3-chunks0] 
dask/array/tests/test_routines.py::test_roll[None-shift3-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[None-shift3-chunks1] 
dask/array/tests/test_routines.py::test_roll[None-shift4-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[None-shift4-chunks0] 
dask/array/tests/test_routines.py::test_roll[None-shift4-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[None-shift4-chunks1] 
dask/array/tests/test_routines.py::test_roll[0-3-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[0-3-chunks0] 
dask/array/tests/test_routines.py::test_roll[0-3-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[0-3-chunks1] 
dask/array/tests/test_routines.py::test_roll[0-7-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[0-7-chunks0] 
dask/array/tests/test_routines.py::test_roll[0-7-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[0-7-chunks1] 
dask/array/tests/test_routines.py::test_roll[0-9-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[0-9-chunks0] 
dask/array/tests/test_routines.py::test_roll[0-9-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[0-9-chunks1] 
dask/array/tests/test_routines.py::test_roll[0-shift3-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[0-shift3-chunks0] 
dask/array/tests/test_routines.py::test_roll[0-shift3-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[0-shift3-chunks1] 
dask/array/tests/test_routines.py::test_roll[0-shift4-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[0-shift4-chunks0] 
dask/array/tests/test_routines.py::test_roll[0-shift4-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[0-shift4-chunks1] 
dask/array/tests/test_routines.py::test_roll[1-3-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[1-3-chunks0] 
dask/array/tests/test_routines.py::test_roll[1-3-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[1-3-chunks1] 
dask/array/tests/test_routines.py::test_roll[1-7-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[1-7-chunks0] 
dask/array/tests/test_routines.py::test_roll[1-7-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[1-7-chunks1] 
dask/array/tests/test_routines.py::test_roll[1-9-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[1-9-chunks0] 
dask/array/tests/test_routines.py::test_roll[1-9-chunks1] 
[gw0] [ 28%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[1-9-chunks1] 
dask/array/tests/test_routines.py::test_roll[1-shift3-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[1-shift3-chunks0] 
dask/array/tests/test_routines.py::test_roll[1-shift3-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[1-shift3-chunks1] 
dask/array/tests/test_routines.py::test_roll[1-shift4-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[1-shift4-chunks0] 
dask/array/tests/test_routines.py::test_roll[1-shift4-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[1-shift4-chunks1] 
dask/array/tests/test_routines.py::test_roll[-1-3-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[-1-3-chunks0] 
dask/array/tests/test_routines.py::test_roll[-1-3-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[-1-3-chunks1] 
dask/array/tests/test_routines.py::test_roll[-1-7-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[-1-7-chunks0] 
dask/array/tests/test_routines.py::test_roll[-1-7-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[-1-7-chunks1] 
dask/array/tests/test_routines.py::test_roll[-1-9-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[-1-9-chunks0] 
dask/array/tests/test_routines.py::test_roll[-1-9-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[-1-9-chunks1] 
dask/array/tests/test_routines.py::test_roll[-1-shift3-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[-1-shift3-chunks0] 
dask/array/tests/test_routines.py::test_roll[-1-shift3-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[-1-shift3-chunks1] 
dask/array/tests/test_routines.py::test_roll[-1-shift4-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[-1-shift4-chunks0] 
dask/array/tests/test_routines.py::test_roll[-1-shift4-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[-1-shift4-chunks1] 
dask/array/tests/test_routines.py::test_roll[axis4-3-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[axis4-3-chunks0] 
dask/array/tests/test_routines.py::test_roll[axis4-3-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[axis4-3-chunks1] 
dask/array/tests/test_routines.py::test_roll[axis4-7-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[axis4-7-chunks0] 
dask/array/tests/test_routines.py::test_roll[axis4-7-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[axis4-7-chunks1] 
dask/array/tests/test_routines.py::test_roll[axis4-9-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[axis4-9-chunks0] 
dask/array/tests/test_routines.py::test_roll[axis4-9-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[axis4-9-chunks1] 
dask/array/tests/test_routines.py::test_roll[axis4-shift3-chunks0] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[axis4-shift3-chunks0] 
dask/array/tests/test_routines.py::test_roll[axis4-shift3-chunks1] 
[gw1] [ 28%] PASSED dask/array/tests/test_routines.py::test_roll[axis4-shift3-chunks1] 
dask/array/tests/test_routines.py::test_roll[axis4-shift4-chunks0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis4-shift4-chunks0] 
dask/array/tests/test_routines.py::test_roll[axis4-shift4-chunks1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis4-shift4-chunks1] 
dask/array/tests/test_routines.py::test_roll[axis5-3-chunks0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis5-3-chunks0] 
dask/array/tests/test_routines.py::test_roll[axis5-3-chunks1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis5-3-chunks1] 
dask/array/tests/test_routines.py::test_roll[axis5-7-chunks0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis5-7-chunks0] 
dask/array/tests/test_routines.py::test_roll[axis5-7-chunks1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis5-7-chunks1] 
dask/array/tests/test_routines.py::test_roll[axis5-9-chunks0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis5-9-chunks0] 
dask/array/tests/test_routines.py::test_roll[axis5-9-chunks1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis5-9-chunks1] 
dask/array/tests/test_routines.py::test_roll[axis5-shift3-chunks0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis5-shift3-chunks0] 
dask/array/tests/test_routines.py::test_roll[axis5-shift3-chunks1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis5-shift3-chunks1] 
dask/array/tests/test_routines.py::test_roll[axis5-shift4-chunks0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis5-shift4-chunks0] 
dask/array/tests/test_routines.py::test_roll[axis5-shift4-chunks1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll[axis5-shift4-chunks1] 
dask/array/tests/test_routines.py::test_roll_always_results_in_a_new_array 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll_always_results_in_a_new_array 
dask/array/tests/test_routines.py::test_roll_works_even_if_shape_is_0 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_roll_works_even_if_shape_is_0 
dask/array/tests/test_routines.py::test_shape_and_ndim[shape0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_shape_and_ndim[shape0] 
dask/array/tests/test_routines.py::test_shape_and_ndim[shape1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_shape_and_ndim[shape1] 
dask/array/tests/test_routines.py::test_shape_and_ndim[shape2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_shape_and_ndim[shape2] 
dask/array/tests/test_routines.py::test_union1d[True-shape0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_union1d[True-shape0] 
dask/array/tests/test_routines.py::test_union1d[True-shape1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_union1d[True-shape1] 
dask/array/tests/test_routines.py::test_union1d[True-shape2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_union1d[True-shape2] 
dask/array/tests/test_routines.py::test_union1d[False-shape0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_union1d[False-shape0] 
dask/array/tests/test_routines.py::test_union1d[False-shape1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_union1d[False-shape1] 
dask/array/tests/test_routines.py::test_union1d[False-shape2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_union1d[False-shape2] 
dask/array/tests/test_routines.py::test_ravel 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-columns] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel 
dask/array/tests/test_routines.py::test_ravel_1D_no_op 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_1D_no_op 
dask/array/tests/test_routines.py::test_ravel_with_array_like 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_with_array_like 
dask/array/tests/test_routines.py::test_expand_dims[None] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_expand_dims[None] 
dask/array/tests/test_routines.py::test_expand_dims[0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_expand_dims[0] 
dask/array/tests/test_routines.py::test_expand_dims[1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_expand_dims[1] 
dask/array/tests/test_routines.py::test_expand_dims[-1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_expand_dims[-1] 
dask/array/tests/test_routines.py::test_expand_dims[axis4] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_expand_dims[axis4] 
dask/array/tests/test_routines.py::test_expand_dims[axis5] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_expand_dims[axis5] 
dask/array/tests/test_routines.py::test_expand_dims[axis6] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_expand_dims[axis6] 
dask/array/tests/test_routines.py::test_expand_dims[2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_expand_dims[2] 
dask/array/tests/test_routines.py::test_squeeze[None-True] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_squeeze[None-True] 
dask/array/tests/test_routines.py::test_squeeze[None-False] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_squeeze[None-False] 
dask/array/tests/test_routines.py::test_squeeze[0-True] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_squeeze[0-True] 
dask/array/tests/test_routines.py::test_squeeze[0-False] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_squeeze[0-False] 
dask/array/tests/test_routines.py::test_squeeze[-1-True] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_squeeze[-1-True] 
dask/array/tests/test_routines.py::test_squeeze[-1-False] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_squeeze[-1-False] 
dask/array/tests/test_routines.py::test_squeeze[axis3-True] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_squeeze[axis3-True] 
dask/array/tests/test_routines.py::test_squeeze[axis3-False] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_squeeze[axis3-False] 
dask/array/tests/test_routines.py::test_squeeze_1d_array[shape0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_squeeze_1d_array[shape0] 
dask/array/tests/test_routines.py::test_squeeze_1d_array[shape1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_squeeze_1d_array[shape1] 
dask/array/tests/test_routines.py::test_vstack 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_vstack 
dask/array/tests/test_routines.py::test_hstack 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_hstack 
dask/array/tests/test_routines.py::test_dstack 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_dstack 
dask/array/tests/test_routines.py::test_stack_unknown_chunk_sizes[hstack-hstack-0] 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_stack_unknown_chunk_sizes[hstack-hstack-0] 
dask/array/tests/test_routines.py::test_stack_unknown_chunk_sizes[dstack-dstack-1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_stack_unknown_chunk_sizes[dstack-dstack-1] 
dask/array/tests/test_routines.py::test_stack_unknown_chunk_sizes[vstack-vstack-2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_stack_unknown_chunk_sizes[vstack-vstack-2] 
dask/array/tests/test_routines.py::test_take 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_take 
dask/array/tests/test_routines.py::test_take_dask_from_numpy 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_take_dask_from_numpy 
dask/array/tests/test_routines.py::test_compress 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-index] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_compress 
dask/array/tests/test_routines.py::test_extract 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_extract 
dask/array/tests/test_routines.py::test_isnull 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_isnull 
dask/array/tests/test_routines.py::test_isnull_result_is_an_array 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_isnull_result_is_an_array 
dask/array/tests/test_routines.py::test_isclose 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_isclose 
dask/array/tests/test_routines.py::test_allclose 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_allclose 
dask/array/tests/test_routines.py::test_choose 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_choose 
dask/array/tests/test_routines.py::test_piecewise 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_piecewise 
dask/array/tests/test_routines.py::test_piecewise_otherwise 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_piecewise_otherwise 
dask/array/tests/test_routines.py::test_select 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_select 
dask/array/tests/test_routines.py::test_select_multidimension 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_select_multidimension 
dask/array/tests/test_routines.py::test_select_return_dtype 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_select_return_dtype 
dask/array/tests/test_routines.py::test_select_broadcasting 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-columns] 
[gw1] [ 29%] XFAIL dask/array/tests/test_routines.py::test_select_broadcasting 
dask/array/tests/test_routines.py::test_argwhere 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_argwhere 
dask/array/tests/test_routines.py::test_argwhere_obj 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_argwhere_obj 
dask/array/tests/test_routines.py::test_argwhere_str 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_argwhere_str 
dask/array/tests/test_routines.py::test_where 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-index] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_where 
dask/array/tests/test_routines.py::test_where_scalar_dtype 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_where_scalar_dtype 
dask/array/tests/test_routines.py::test_where_bool_optimization 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_where_bool_optimization 
dask/array/tests/test_routines.py::test_where_nonzero 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_where_nonzero 
dask/array/tests/test_routines.py::test_where_incorrect_args 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_where_incorrect_args 
dask/array/tests/test_routines.py::test_count_nonzero 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_count_nonzero 
dask/array/tests/test_routines.py::test_count_nonzero_axis[None] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_count_nonzero_axis[None] 
dask/array/tests/test_routines.py::test_count_nonzero_axis[0] 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_count_nonzero_axis[0] 
dask/array/tests/test_routines.py::test_count_nonzero_axis[axis2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_count_nonzero_axis[axis2] 
dask/array/tests/test_routines.py::test_count_nonzero_axis[axis3] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_count_nonzero_axis[axis3] 
dask/array/tests/test_routines.py::test_count_nonzero_obj 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_count_nonzero_obj 
dask/array/tests/test_routines.py::test_count_nonzero_obj_axis[None] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_count_nonzero_obj_axis[None] 
dask/array/tests/test_routines.py::test_count_nonzero_obj_axis[0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_count_nonzero_obj_axis[0] 
dask/array/tests/test_routines.py::test_count_nonzero_obj_axis[axis2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_count_nonzero_obj_axis[axis2] 
dask/array/tests/test_routines.py::test_count_nonzero_obj_axis[axis3] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_count_nonzero_obj_axis[axis3] 
dask/array/tests/test_routines.py::test_count_nonzero_str 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_count_nonzero_str 
dask/array/tests/test_routines.py::test_flatnonzero 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_flatnonzero 
dask/array/tests/test_routines.py::test_nonzero 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-columns] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_nonzero 
dask/array/tests/test_routines.py::test_nonzero_method 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_nonzero_method 
dask/array/tests/test_routines.py::test_unravel_index_empty 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_unravel_index_empty 
dask/array/tests/test_routines.py::test_unravel_index 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_unravel_index 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr0-chunks0-kwargs0-<lambda>0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr0-chunks0-kwargs0-<lambda>0] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr0-chunks0-kwargs0-<lambda>1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr0-chunks0-kwargs0-<lambda>1] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr0-chunks0-kwargs0-<lambda>2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr0-chunks0-kwargs0-<lambda>2] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr0-chunks0-kwargs0-asarray] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr0-chunks0-kwargs0-asarray] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr0-chunks0-kwargs0-from_array] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr0-chunks0-kwargs0-from_array] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr1-chunks1-kwargs1-<lambda>0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr1-chunks1-kwargs1-<lambda>0] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr1-chunks1-kwargs1-<lambda>1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr1-chunks1-kwargs1-<lambda>1] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr1-chunks1-kwargs1-<lambda>2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr1-chunks1-kwargs1-<lambda>2] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr1-chunks1-kwargs1-asarray] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr1-chunks1-kwargs1-asarray] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr1-chunks1-kwargs1-from_array] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr1-chunks1-kwargs1-from_array] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr2-1-kwargs2-<lambda>0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr2-1-kwargs2-<lambda>0] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr2-1-kwargs2-<lambda>1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr2-1-kwargs2-<lambda>1] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr2-1-kwargs2-<lambda>2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr2-1-kwargs2-<lambda>2] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr2-1-kwargs2-asarray] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr2-1-kwargs2-asarray] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr2-1-kwargs2-from_array] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr2-1-kwargs2-from_array] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr3-chunks3-kwargs3-<lambda>0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr3-chunks3-kwargs3-<lambda>0] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr3-chunks3-kwargs3-<lambda>1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr3-chunks3-kwargs3-<lambda>1] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr3-chunks3-kwargs3-<lambda>2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr3-chunks3-kwargs3-<lambda>2] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr3-chunks3-kwargs3-asarray] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr3-chunks3-kwargs3-asarray] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr3-chunks3-kwargs3-from_array] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr3-chunks3-kwargs3-from_array] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr4-chunks4-kwargs4-<lambda>0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr4-chunks4-kwargs4-<lambda>0] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr4-chunks4-kwargs4-<lambda>1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr4-chunks4-kwargs4-<lambda>1] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr4-chunks4-kwargs4-<lambda>2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr4-chunks4-kwargs4-<lambda>2] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr4-chunks4-kwargs4-asarray] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr4-chunks4-kwargs4-asarray] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr4-chunks4-kwargs4-from_array] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr4-chunks4-kwargs4-from_array] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr5-chunks5-kwargs5-<lambda>0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr5-chunks5-kwargs5-<lambda>0] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr5-chunks5-kwargs5-<lambda>1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr5-chunks5-kwargs5-<lambda>1] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr5-chunks5-kwargs5-<lambda>2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr5-chunks5-kwargs5-<lambda>2] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr5-chunks5-kwargs5-asarray] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr5-chunks5-kwargs5-asarray] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr5-chunks5-kwargs5-from_array] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr5-chunks5-kwargs5-from_array] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr6-chunks6-kwargs6-<lambda>0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr6-chunks6-kwargs6-<lambda>0] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr6-chunks6-kwargs6-<lambda>1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr6-chunks6-kwargs6-<lambda>1] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr6-chunks6-kwargs6-<lambda>2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr6-chunks6-kwargs6-<lambda>2] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr6-chunks6-kwargs6-asarray] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr6-chunks6-kwargs6-asarray] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr6-chunks6-kwargs6-from_array] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr6-chunks6-kwargs6-from_array] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr7-None-kwargs7-<lambda>0] 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr7-None-kwargs7-<lambda>0] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr7-None-kwargs7-<lambda>1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-index] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr7-None-kwargs7-<lambda>1] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr7-None-kwargs7-<lambda>2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr7-None-kwargs7-<lambda>2] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr7-None-kwargs7-asarray] 
[gw1] [ 29%] SKIPPED dask/array/tests/test_routines.py::test_ravel_multi_index[arr7-None-kwargs7-asarray] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr7-None-kwargs7-from_array] 
[gw1] [ 29%] SKIPPED dask/array/tests/test_routines.py::test_ravel_multi_index[arr7-None-kwargs7-from_array] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr8-None-kwargs8-<lambda>0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr8-None-kwargs8-<lambda>0] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr8-None-kwargs8-<lambda>1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr8-None-kwargs8-<lambda>1] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr8-None-kwargs8-<lambda>2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index[arr8-None-kwargs8-<lambda>2] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr8-None-kwargs8-asarray] 
[gw1] [ 29%] SKIPPED dask/array/tests/test_routines.py::test_ravel_multi_index[arr8-None-kwargs8-asarray] 
dask/array/tests/test_routines.py::test_ravel_multi_index[arr8-None-kwargs8-from_array] 
[gw1] [ 29%] SKIPPED dask/array/tests/test_routines.py::test_ravel_multi_index[arr8-None-kwargs8-from_array] 
dask/array/tests/test_routines.py::test_ravel_multi_index_unknown_shape 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index_unknown_shape 
dask/array/tests/test_routines.py::test_ravel_multi_index_unknown_shape_fails 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index_unknown_shape_fails 
dask/array/tests/test_routines.py::test_ravel_multi_index_delayed_dims[False-dims0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index_delayed_dims[False-dims0] 
dask/array/tests/test_routines.py::test_ravel_multi_index_delayed_dims[False-dims1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index_delayed_dims[False-dims1] 
dask/array/tests/test_routines.py::test_ravel_multi_index_delayed_dims[True-dims0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index_delayed_dims[True-dims0] 
dask/array/tests/test_routines.py::test_ravel_multi_index_delayed_dims[True-dims1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index_delayed_dims[True-dims1] 
dask/array/tests/test_routines.py::test_ravel_multi_index_non_int_dtype 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_ravel_multi_index_non_int_dtype 
dask/array/tests/test_routines.py::test_coarsen 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen 
dask/array/tests/test_routines.py::test_coarsen_with_excess 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_with_excess 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks0] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks1] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks2] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks3] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks3] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks4] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks4] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks5] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks5] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks6] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks6] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks7] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks7] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks8] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks8] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks9] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks9] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks10] 
[gw0] [ 29%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-0] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks10] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks11] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks11] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks12] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks12] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks13] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks13] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks14] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks14] 
dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks15] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_coarsen_bad_chunks[chunks15] 
dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks0-1] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks0-1] 
dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks1-2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks1-2] 
dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks2-2] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks2-2] 
dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks3-10] 
[gw1] [ 29%] PASSED dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks3-10] 
dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks4-10] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks4-10] 
dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks5-8] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks5-8] 
dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks6-10] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks6-10] 
dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks7-16] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks7-16] 
dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks8-10] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks8-10] 
dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks9-4] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_aligned_coarsen_chunks[chunks9-4] 
dask/array/tests/test_routines.py::test_insert 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_insert 
dask/array/tests/test_routines.py::test_append 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-columns] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_append 
dask/array/tests/test_routines.py::test_multi_insert 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_multi_insert 
dask/array/tests/test_routines.py::test_delete 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_delete 
dask/array/tests/test_routines.py::test_result_type 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_result_type 
dask/array/tests/test_routines.py::test_einsum[abc,bad->abcd] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[abc,bad->abcd] 
dask/array/tests/test_routines.py::test_einsum[abcdef,bcdfg->abcdeg] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-1] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[abcdef,bcdfg->abcdeg] 
dask/array/tests/test_routines.py::test_einsum[ea,fb,abcd,gc,hd->efgh] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-index] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-0] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[ea,fb,abcd,gc,hd->efgh] 
dask/array/tests/test_routines.py::test_einsum[ab,b] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[ab,b] 
dask/array/tests/test_routines.py::test_einsum[aa] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[aa] 
dask/array/tests/test_routines.py::test_einsum[a,a->] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[a,a->] 
dask/array/tests/test_routines.py::test_einsum[a,a->a] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[a,a->a] 
dask/array/tests/test_routines.py::test_einsum[a,a] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[a,a] 
dask/array/tests/test_routines.py::test_einsum[a,b] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[a,b] 
dask/array/tests/test_routines.py::test_einsum[a,b,c] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[a,b,c] 
dask/array/tests/test_routines.py::test_einsum[a] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[a] 
dask/array/tests/test_routines.py::test_einsum[ba,b] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[ba,b] 
dask/array/tests/test_routines.py::test_einsum[ba,b->] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[ba,b->] 
dask/array/tests/test_routines.py::test_einsum[defab,fedbc->defac] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-columns] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[defab,fedbc->defac] 
dask/array/tests/test_routines.py::test_einsum[ab...,bc...->ac...] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[ab...,bc...->ac...] 
dask/array/tests/test_routines.py::test_einsum[a...a] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[a...a] 
dask/array/tests/test_routines.py::test_einsum[abc...->cba...] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[abc...->cba...] 
dask/array/tests/test_routines.py::test_einsum[...ab->...a] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[...ab->...a] 
dask/array/tests/test_routines.py::test_einsum[a...a->a...] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[a...a->a...] 
dask/array/tests/test_routines.py::test_einsum[...abc,...abcd->...d] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[...abc,...abcd->...d] 
dask/array/tests/test_routines.py::test_einsum[ab...,b->ab...] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[ab...,b->ab...] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-1] 
dask/array/tests/test_routines.py::test_einsum[aa->a] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[aa->a] 
dask/array/tests/test_routines.py::test_einsum[ab,ab,c->c] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[ab,ab,c->c] 
dask/array/tests/test_routines.py::test_einsum[aab,bc->ac] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[aab,bc->ac] 
dask/array/tests/test_routines.py::test_einsum[aab,bcc->ac] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[aab,bcc->ac] 
dask/array/tests/test_routines.py::test_einsum[fdf,cdd,ccd,afe->ae] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[fdf,cdd,ccd,afe->ae] 
dask/array/tests/test_routines.py::test_einsum[fff,fae,bef,def->abd] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum[fff,fae,bef,def->abd] 
dask/array/tests/test_routines.py::test_einsum_chunksizes 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-index] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-0] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-columns] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_chunksizes 
dask/array/tests/test_routines.py::test_einsum_optimize[optimize_opts0] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_optimize[optimize_opts0] 
dask/array/tests/test_routines.py::test_einsum_optimize[optimize_opts1] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_optimize[optimize_opts1] 
dask/array/tests/test_routines.py::test_einsum_optimize[optimize_opts2] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_optimize[optimize_opts2] 
dask/array/tests/test_routines.py::test_einsum_order[C] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_order[C] 
dask/array/tests/test_routines.py::test_einsum_order[F] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-1] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_order[F] 
dask/array/tests/test_routines.py::test_einsum_order[A] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_order[A] 
dask/array/tests/test_routines.py::test_einsum_order[K] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_order[K] 
dask/array/tests/test_routines.py::test_einsum_casting[no] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_casting[no] 
dask/array/tests/test_routines.py::test_einsum_casting[equiv] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_casting[equiv] 
dask/array/tests/test_routines.py::test_einsum_casting[safe] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_casting[safe] 
dask/array/tests/test_routines.py::test_einsum_casting[same_kind] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_casting[same_kind] 
dask/array/tests/test_routines.py::test_einsum_casting[unsafe] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_casting[unsafe] 
dask/array/tests/test_routines.py::test_einsum_split_every[None] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_split_every[None] 
dask/array/tests/test_routines.py::test_einsum_split_every[2] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_split_every[2] 
dask/array/tests/test_routines.py::test_einsum_invalid_args 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_invalid_args 
dask/array/tests/test_routines.py::test_einsum_broadcasting_contraction 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_broadcasting_contraction 
dask/array/tests/test_routines.py::test_einsum_broadcasting_contraction2 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-index] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_broadcasting_contraction2 
dask/array/tests/test_routines.py::test_einsum_broadcasting_contraction3 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_broadcasting_contraction3 
dask/array/tests/test_routines.py::test_einsum_empty_dimension 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_einsum_empty_dimension 
dask/array/tests/test_routines.py::test_average[True-a0] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_average[True-a0] 
dask/array/tests/test_routines.py::test_average[True-a1] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_average[True-a1] 
dask/array/tests/test_routines.py::test_average[False-a0] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_average[False-a0] 
dask/array/tests/test_routines.py::test_average[False-a1] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_average[False-a1] 
dask/array/tests/test_routines.py::test_average_keepdims[a0] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_average_keepdims[a0] 
dask/array/tests/test_routines.py::test_average_keepdims[a1] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_average_keepdims[a1] 
dask/array/tests/test_routines.py::test_average_weights[False] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_average_weights[False] 
dask/array/tests/test_routines.py::test_average_weights[True] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_average_weights[True] 
dask/array/tests/test_routines.py::test_average_raises 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_average_raises 
dask/array/tests/test_routines.py::test_iscomplexobj 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_iscomplexobj 
dask/array/tests/test_routines.py::test_tril_triu 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-0] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-columns] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-1] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-index] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-0] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-columns] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-1] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-index] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-0] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-columns] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_tril_triu 
dask/array/tests/test_routines.py::test_tril_ndims 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_tril_ndims 
dask/array/tests/test_routines.py::test_tril_triu_non_square_arrays 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_tril_triu_non_square_arrays 
dask/array/tests/test_routines.py::test_tril_triu_indices[3-0-3-auto] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_tril_triu_indices[3-0-3-auto] 
dask/array/tests/test_routines.py::test_tril_triu_indices[3-1-3-auto] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_tril_triu_indices[3-1-3-auto] 
dask/array/tests/test_routines.py::test_tril_triu_indices[3--1-3-auto] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_tril_triu_indices[3--1-3-auto] 
dask/array/tests/test_routines.py::test_tril_triu_indices[5-0-5-1] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-1] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-index] 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_tril_triu_indices[5-0-5-1] 
dask/array/tests/test_routines.py::test_pickle_vectorized_routines 
[gw1] [ 30%] PASSED dask/array/tests/test_routines.py::test_pickle_vectorized_routines 
dask/array/tests/test_shuffle.py::test_shuffle[indexer0-chunks0-other_chunks0] 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_shuffle[indexer0-chunks0-other_chunks0] 
dask/array/tests/test_shuffle.py::test_shuffle[indexer1-chunks1-other_chunks1] 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_shuffle[indexer1-chunks1-other_chunks1] 
dask/array/tests/test_shuffle.py::test_shuffle[indexer2-chunks2-other_chunks2] 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_shuffle[indexer2-chunks2-other_chunks2] 
dask/array/tests/test_shuffle.py::test_shuffle[indexer3-chunks3-other_chunks3] 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_shuffle[indexer3-chunks3-other_chunks3] 
dask/array/tests/test_shuffle.py::test_shuffle_config_tolerance[1-chunks0] 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_shuffle_config_tolerance[1-chunks0] 
dask/array/tests/test_shuffle.py::test_shuffle_config_tolerance[1.4-chunks1] 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_shuffle_config_tolerance[1.4-chunks1] 
dask/array/tests/test_shuffle.py::test_shuffle_larger_array 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-0] 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_shuffle_larger_array 
dask/array/tests/test_shuffle.py::test_incompatible_indexer 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_incompatible_indexer 
dask/array/tests/test_shuffle.py::test_unknown_chunk_sizes 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_unknown_chunk_sizes 
dask/array/tests/test_shuffle.py::test_oob_axis 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_oob_axis 
dask/array/tests/test_shuffle.py::test_oob_indexer 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_oob_indexer 
dask/array/tests/test_shuffle.py::test_shuffle_no_op_with_correct_indexer 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_shuffle_no_op_with_correct_indexer 
dask/array/tests/test_shuffle.py::test_resize_other_dimensions 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-columns] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-1] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-index] 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_resize_other_dimensions 
dask/array/tests/test_shuffle.py::test_dtype_taker 
[gw1] [ 30%] PASSED dask/array/tests/test_shuffle.py::test_dtype_taker 
dask/array/tests/test_slicing.py::test_slice_1d 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slice_1d 
dask/array/tests/test_slicing.py::test_slice_singleton_value_on_boundary 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slice_singleton_value_on_boundary 
dask/array/tests/test_slicing.py::test_slice_array_1d 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slice_array_1d 
dask/array/tests/test_slicing.py::test_slice_array_2d 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slice_array_2d 
dask/array/tests/test_slicing.py::test_slice_optimizations 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slice_optimizations 
dask/array/tests/test_slicing.py::test_slicing_with_singleton_indices 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_singleton_indices 
dask/array/tests/test_slicing.py::test_slicing_with_newaxis 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_newaxis 
dask/array/tests/test_slicing.py::test_take 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_take 
dask/array/tests/test_slicing.py::test_take_sorted 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_take_sorted 
dask/array/tests/test_slicing.py::test_slicing_chunks 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slicing_chunks 
dask/array/tests/test_slicing.py::test_slicing_with_numpy_arrays 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_numpy_arrays 
dask/array/tests/test_slicing.py::test_slicing_and_chunks 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slicing_and_chunks 
dask/array/tests/test_slicing.py::test_slicing_and_unknown_chunks 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slicing_and_unknown_chunks 
dask/array/tests/test_slicing.py::test_slicing_identities 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slicing_identities 
dask/array/tests/test_slicing.py::test_slice_stop_0 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slice_stop_0 
dask/array/tests/test_slicing.py::test_slice_list_then_None 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slice_list_then_None 
dask/array/tests/test_slicing.py::test_slicing_exhaustively 
[gw1] [ 30%] SKIPPED dask/array/tests/test_slicing.py::test_slicing_exhaustively 
dask/array/tests/test_slicing.py::test_slicing_with_negative_step_flops_keys 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_negative_step_flops_keys 
dask/array/tests/test_slicing.py::test_empty_slice 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_empty_slice 
dask/array/tests/test_slicing.py::test_multiple_list_slicing 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_multiple_list_slicing 
dask/array/tests/test_slicing.py::test_boolean_list_slicing 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_boolean_list_slicing 
dask/array/tests/test_slicing.py::test_boolean_numpy_array_slicing 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_boolean_numpy_array_slicing 
dask/array/tests/test_slicing.py::test_empty_list 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-0] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_empty_list 
dask/array/tests/test_slicing.py::test_uneven_chunks 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_uneven_chunks 
dask/array/tests/test_slicing.py::test_new_blockdim 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_new_blockdim 
dask/array/tests/test_slicing.py::test_slicing_consistent_names 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slicing_consistent_names 
dask/array/tests/test_slicing.py::test_slicing_consistent_names_after_normalization 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_slicing_consistent_names_after_normalization 
dask/array/tests/test_slicing.py::test_sanitize_index_element 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_sanitize_index_element 
dask/array/tests/test_slicing.py::test_sanitize_index 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_sanitize_index 
dask/array/tests/test_slicing.py::test_uneven_blockdims 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_uneven_blockdims 
dask/array/tests/test_slicing.py::test_oob_check 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_oob_check 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[None-None] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[None-None] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[None-3] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[None-3] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[None-2] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[None-2] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[None-1] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[None-1] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks1-None] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks1-None] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks1-3] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks1-3] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks1-2] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks1-2] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks1-1] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks1-1] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks2-None] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks2-None] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks2-3] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks2-3] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks2-2] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks2-2] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks2-1] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-columns] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks2-1] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks3-None] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks3-None] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks3-3] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks3-3] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks3-2] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks3-2] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks3-1] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks3-1] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks4-None] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks4-None] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks4-3] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks4-3] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks4-2] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks4-2] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks4-1] 
[gw0] [ 30%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-1] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array[x_chunks4-1] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_0d[1] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_0d[1] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_0d[2] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_0d[2] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_0d[3] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_0d[3] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nanchunks[1] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nanchunks[1] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nanchunks[2] 
[gw1] [ 30%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nanchunks[2] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nanchunks[3] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-index] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nanchunks[3] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nanchunks[4] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nanchunks[4] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nanchunks[5] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nanchunks[5] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_negindex[2] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_negindex[2] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_negindex[4] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_negindex[4] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_indexerror[2] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_indexerror[2] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_indexerror[4] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_indexerror[4] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[int8] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[int8] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[int16] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[int16] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[int32] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[int32] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[int64] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[int64] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[uint8] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[uint8] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[uint16] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[uint16] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[uint32] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[uint32] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[uint64] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_dtypes[uint64] 
dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nocompute 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_int_dask_array_nocompute 
dask/array/tests/test_slicing.py::test_index_with_bool_dask_array 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-0] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_bool_dask_array 
dask/array/tests/test_slicing.py::test_index_with_bool_dask_array_2 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_index_with_bool_dask_array_2 
dask/array/tests/test_slicing.py::test_cull 
[gw1] [ 31%] XFAIL dask/array/tests/test_slicing.py::test_cull 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index0-shape0] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index0-shape0] 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index0-shape1] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index0-shape1] 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index0-shape2] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index0-shape2] 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index1-shape0] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index1-shape0] 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index1-shape1] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index1-shape1] 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index1-shape2] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index1-shape2] 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index2-shape0] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index2-shape0] 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index2-shape1] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index2-shape1] 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index2-shape2] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index2-shape2] 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index3-shape0] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index3-shape0] 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index3-shape1] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index3-shape1] 
dask/array/tests/test_slicing.py::test_slicing_with_Nones[index3-shape2] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_with_Nones[index3-shape2] 
dask/array/tests/test_slicing.py::test_slicing_integer_no_warnings 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slicing_integer_no_warnings 
dask/array/tests/test_slicing.py::test_slicing_none_int_ellipes 
[gw1] [ 31%] SKIPPED dask/array/tests/test_slicing.py::test_slicing_none_int_ellipes 
dask/array/tests/test_slicing.py::test_None_overlap_int 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_None_overlap_int 
dask/array/tests/test_slicing.py::test_negative_n_slicing 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-columns] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_negative_n_slicing 
dask/array/tests/test_slicing.py::test_negative_list_slicing 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_negative_list_slicing 
dask/array/tests/test_slicing.py::test_permit_oob_slices 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_permit_oob_slices 
dask/array/tests/test_slicing.py::test_normalize_index 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_normalize_index 
dask/array/tests/test_slicing.py::test_take_semi_sorted 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_take_semi_sorted 
dask/array/tests/test_slicing.py::test_getitem_avoids_large_chunks 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-1] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-index] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-0] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-columns] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-1] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-index] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-0] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_getitem_avoids_large_chunks 
dask/array/tests/test_slicing.py::test_getitem_avoids_large_chunks_missing 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-columns] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-1] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-index] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-0] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_getitem_avoids_large_chunks_missing 
dask/array/tests/test_slicing.py::test_take_avoids_large_chunks 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-columns] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_take_avoids_large_chunks 
dask/array/tests/test_slicing.py::test_take_uses_config 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_take_uses_config 
dask/array/tests/test_slicing.py::test_pathological_unsorted_slicing 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_pathological_unsorted_slicing 
dask/array/tests/test_slicing.py::test_setitem_with_different_chunks_preserves_shape[params0] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_setitem_with_different_chunks_preserves_shape[params0] 
dask/array/tests/test_slicing.py::test_setitem_with_different_chunks_preserves_shape[params1] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_setitem_with_different_chunks_preserves_shape[params1] 
dask/array/tests/test_slicing.py::test_gh3579 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_gh3579 
dask/array/tests/test_slicing.py::test_make_blockwise_sorted_slice 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_make_blockwise_sorted_slice 
dask/array/tests/test_slicing.py::test_shuffle_slice[size0-chunks0] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_shuffle_slice[size0-chunks0] 
dask/array/tests/test_slicing.py::test_shuffle_slice[size1-chunks1] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_shuffle_slice[size1-chunks1] 
dask/array/tests/test_slicing.py::test_shuffle_slice[size2-chunks2] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_shuffle_slice[size2-chunks2] 
dask/array/tests/test_slicing.py::test_unknown_chunks_length_one 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_unknown_chunks_length_one 
dask/array/tests/test_slicing.py::test_gh4043[True-True-True] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_gh4043[True-True-True] 
dask/array/tests/test_slicing.py::test_gh4043[True-True-False] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_gh4043[True-True-False] 
dask/array/tests/test_slicing.py::test_gh4043[True-False-True] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_gh4043[True-False-True] 
dask/array/tests/test_slicing.py::test_gh4043[True-False-False] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_gh4043[True-False-False] 
dask/array/tests/test_slicing.py::test_gh4043[False-True-True] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_gh4043[False-True-True] 
dask/array/tests/test_slicing.py::test_gh4043[False-True-False] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_gh4043[False-True-False] 
dask/array/tests/test_slicing.py::test_gh4043[False-False-True] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_gh4043[False-False-True] 
dask/array/tests/test_slicing.py::test_gh4043[False-False-False] 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_gh4043[False-False-False] 
dask/array/tests/test_slicing.py::test_slice_array_3d_with_bool_numpy_array 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slice_array_3d_with_bool_numpy_array 
dask/array/tests/test_slicing.py::test_slice_masked_arrays 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slice_masked_arrays 
dask/array/tests/test_slicing.py::test_slice_array_null_dimension 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_slice_array_null_dimension 
dask/array/tests/test_slicing.py::test_take_sorted_indexer 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_take_sorted_indexer 
dask/array/tests/test_slicing.py::test_all_none_slices_just_mappings 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_all_none_slices_just_mappings 
dask/array/tests/test_slicing.py::test_minimal_dtype_doesnt_overflow 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_minimal_dtype_doesnt_overflow 
dask/array/tests/test_slicing.py::test_vindex_with_dask_array 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_vindex_with_dask_array 
dask/array/tests/test_slicing.py::test_positional_indexer_newaxis 
[gw1] [ 31%] PASSED dask/array/tests/test_slicing.py::test_positional_indexer_newaxis 
dask/array/tests/test_stats.py::test_measures[True-skew-kwargs0] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-index] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_measures[True-skew-kwargs0] 
dask/array/tests/test_stats.py::test_measures[True-kurtosis-kwargs1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_measures[True-kurtosis-kwargs1] 
dask/array/tests/test_stats.py::test_measures[True-kurtosis-kwargs2] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_measures[True-kurtosis-kwargs2] 
dask/array/tests/test_stats.py::test_measures[False-skew-kwargs0] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_measures[False-skew-kwargs0] 
dask/array/tests/test_stats.py::test_measures[False-kurtosis-kwargs1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_measures[False-kurtosis-kwargs1] 
dask/array/tests/test_stats.py::test_measures[False-kurtosis-kwargs2] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_measures[False-kurtosis-kwargs2] 
dask/array/tests/test_stats.py::test_bias_raises 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_bias_raises 
dask/array/tests/test_stats.py::test_one[chisquare] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_one[chisquare] 
dask/array/tests/test_stats.py::test_one[power_divergence] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_one[power_divergence] 
dask/array/tests/test_stats.py::test_one[normaltest] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-0] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_one[normaltest] 
dask/array/tests/test_stats.py::test_one[skewtest] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_one[skewtest] 
dask/array/tests/test_stats.py::test_one[kurtosistest] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_one[kurtosistest] 
dask/array/tests/test_stats.py::test_two[ttest_ind-kwargs0] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-columns] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_two[ttest_ind-kwargs0] 
dask/array/tests/test_stats.py::test_two[ttest_ind-kwargs1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_two[ttest_ind-kwargs1] 
dask/array/tests/test_stats.py::test_two[ttest_1samp-kwargs2] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-1] 
[gw1] [ 31%] XFAIL dask/array/tests/test_stats.py::test_two[ttest_1samp-kwargs2] 
dask/array/tests/test_stats.py::test_two[ttest_rel-kwargs3] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_two[ttest_rel-kwargs3] 
dask/array/tests/test_stats.py::test_two[chisquare-kwargs4] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_two[chisquare-kwargs4] 
dask/array/tests/test_stats.py::test_two[power_divergence-kwargs5] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_two[power_divergence-kwargs5] 
dask/array/tests/test_stats.py::test_two[power_divergence-kwargs6] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_two[power_divergence-kwargs6] 
dask/array/tests/test_stats.py::test_two[power_divergence-kwargs7] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_two[power_divergence-kwargs7] 
dask/array/tests/test_stats.py::test_two[power_divergence-kwargs8] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-index] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_two[power_divergence-kwargs8] 
dask/array/tests/test_stats.py::test_moments[0] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_moments[0] 
dask/array/tests/test_stats.py::test_moments[1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_moments[1] 
dask/array/tests/test_stats.py::test_moments[2] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_moments[2] 
dask/array/tests/test_stats.py::test_moments[3] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_moments[3] 
dask/array/tests/test_stats.py::test_moments[4] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_moments[4] 
dask/array/tests/test_stats.py::test_anova 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_anova 
dask/array/tests/test_stats.py::test_nan_raises[omit-ttest_1samp-2] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[omit-ttest_1samp-2] 
dask/array/tests/test_stats.py::test_nan_raises[omit-ttest_rel-2] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[omit-ttest_rel-2] 
dask/array/tests/test_stats.py::test_nan_raises[omit-skewtest-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[omit-skewtest-1] 
dask/array/tests/test_stats.py::test_nan_raises[omit-kurtosis-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[omit-kurtosis-1] 
dask/array/tests/test_stats.py::test_nan_raises[omit-kurtosistest-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[omit-kurtosistest-1] 
dask/array/tests/test_stats.py::test_nan_raises[omit-normaltest-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[omit-normaltest-1] 
dask/array/tests/test_stats.py::test_nan_raises[omit-moment-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[omit-moment-1] 
dask/array/tests/test_stats.py::test_nan_raises[raise-ttest_1samp-2] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[raise-ttest_1samp-2] 
dask/array/tests/test_stats.py::test_nan_raises[raise-ttest_rel-2] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[raise-ttest_rel-2] 
dask/array/tests/test_stats.py::test_nan_raises[raise-skewtest-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[raise-skewtest-1] 
dask/array/tests/test_stats.py::test_nan_raises[raise-kurtosis-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[raise-kurtosis-1] 
dask/array/tests/test_stats.py::test_nan_raises[raise-kurtosistest-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[raise-kurtosistest-1] 
dask/array/tests/test_stats.py::test_nan_raises[raise-normaltest-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[raise-normaltest-1] 
dask/array/tests/test_stats.py::test_nan_raises[raise-moment-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_nan_raises[raise-moment-1] 
dask/array/tests/test_stats.py::test_power_divergence_invalid 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_power_divergence_invalid 
dask/array/tests/test_stats.py::test_skew_raises 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_skew_raises 
dask/array/tests/test_stats.py::test_skew_single_return_type 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_skew_single_return_type 
dask/array/tests/test_stats.py::test_kurtosis_single_return_type 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-0] 
[gw1] [ 31%] PASSED dask/array/tests/test_stats.py::test_kurtosis_single_return_type 
dask/array/tests/test_svg.py::test_basic 
[gw1] [ 31%] PASSED dask/array/tests/test_svg.py::test_basic 
dask/array/tests/test_svg.py::test_repr_html 
[gw1] [ 31%] SKIPPED dask/array/tests/test_svg.py::test_repr_html 
dask/array/tests/test_svg.py::test_errors 
[gw1] [ 31%] PASSED dask/array/tests/test_svg.py::test_errors 
dask/array/tests/test_svg.py::test_repr_html_size_units 
[gw1] [ 31%] SKIPPED dask/array/tests/test_svg.py::test_repr_html_size_units 
dask/array/tests/test_svg.py::test_draw_sizes 
[gw1] [ 31%] PASSED dask/array/tests/test_svg.py::test_draw_sizes 
dask/array/tests/test_svg.py::test_too_many_lines_fills_sides_darker 
[gw1] [ 31%] PASSED dask/array/tests/test_svg.py::test_too_many_lines_fills_sides_darker 
dask/array/tests/test_svg.py::test_3d 
[gw1] [ 31%] PASSED dask/array/tests/test_svg.py::test_3d 
dask/array/tests/test_testing.py::test_assert_eq_checks_scalars 
[gw1] [ 31%] PASSED dask/array/tests/test_testing.py::test_assert_eq_checks_scalars 
dask/array/tests/test_ufunc.py::test_ufunc_meta[log] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_meta[log] 
dask/array/tests/test_ufunc.py::test_ufunc_meta[modf] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_meta[modf] 
dask/array/tests/test_ufunc.py::test_ufunc_meta[frexp] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_meta[frexp] 
dask/array/tests/test_ufunc.py::test_ufunc 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_ufunc 
dask/array/tests/test_ufunc.py::test_unary_ufunc[abs] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[abs] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[absolute] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[absolute] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[arccos] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[arccos] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[arccosh] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[arccosh] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[arcsin] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-columns] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[arcsin] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[arcsinh] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[arcsinh] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[arctan] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[arctan] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[arctanh] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[arctanh] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[bitwise_not] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[bitwise_not] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[cbrt] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-1] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[cbrt] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[ceil] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[ceil] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[conj] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[conj] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[cos] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[cos] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[cosh] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[cosh] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[deg2rad] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[deg2rad] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[degrees] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-index] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[degrees] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[exp] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[exp] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[exp2] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[exp2] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[expm1] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[expm1] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[fabs] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[fabs] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[fix] 
[gw1] [ 31%] SKIPPED dask/array/tests/test_ufunc.py::test_unary_ufunc[fix] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[floor] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[floor] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[invert] 
[gw0] [ 31%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-0] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[invert] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[isfinite] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[isfinite] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[isinf] 
[gw1] [ 31%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[isinf] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[isnan] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-columns] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[isnan] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[log] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[log] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[log10] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[log10] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[log1p] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-columns] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[log1p] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-1] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[log2] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[log2] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[logical_not] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[logical_not] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[negative] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[negative] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[positive] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[positive] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[rad2deg] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[rad2deg] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[radians] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-index] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[radians] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[reciprocal] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[reciprocal] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[rint] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[rint] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[sign] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[sign] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[signbit] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[signbit] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[sin] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[sin] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[sinh] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-0] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[sinh] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[spacing] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[spacing] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[sqrt] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[sqrt] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[square] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[square] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[tan] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[tan] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[tanh] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-columns] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[tanh] 
dask/array/tests/test_ufunc.py::test_unary_ufunc[trunc] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unary_ufunc[trunc] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[add] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[add] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[arctan2] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[arctan2] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[copysign] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-1] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[copysign] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[divide] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[divide] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[equal] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[equal] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-index] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[bitwise_and] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[bitwise_and] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[bitwise_or] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[bitwise_or] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[bitwise_xor] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-0] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[bitwise_xor] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[floor_divide] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[floor_divide] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[fmax] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[fmax] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[fmin] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-columns] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[fmin] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[fmod] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[fmod] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[greater] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[greater] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[greater_equal] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-1] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[greater_equal] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[hypot] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[hypot] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[ldexp] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[ldexp] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[left_shift] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-index] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[left_shift] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[less] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[less] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[less_equal] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[less_equal] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[logaddexp] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-0] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-columns] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[logaddexp] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[logaddexp2] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[logaddexp2] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[logical_and] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[logical_and] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[logical_or] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-1] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[logical_or] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[logical_xor] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[logical_xor] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[maximum] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[maximum] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[minimum] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-index] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[minimum] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[mod] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[mod] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[multiply] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[multiply] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[nextafter] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[nextafter] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[not_equal] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[not_equal] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[power] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[power] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[remainder] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-0] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[remainder] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[right_shift] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[right_shift] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[subtract] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[subtract] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[true_divide] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-columns] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[true_divide] 
dask/array/tests/test_ufunc.py::test_binary_ufunc[float_power] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_binary_ufunc[float_power] 
dask/array/tests/test_ufunc.py::test_ufunc_outer 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_outer 
dask/array/tests/test_ufunc.py::test_complex[isreal] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-1] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_complex[isreal] 
dask/array/tests/test_ufunc.py::test_complex[iscomplex] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-index] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_complex[iscomplex] 
dask/array/tests/test_ufunc.py::test_complex[real] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-0] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_complex[real] 
dask/array/tests/test_ufunc.py::test_complex[imag] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_complex[imag] 
dask/array/tests/test_ufunc.py::test_ufunc_2results[frexp] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-columns] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_2results[frexp] 
dask/array/tests/test_ufunc.py::test_ufunc_2results[modf] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_2results[modf] 
dask/array/tests/test_ufunc.py::test_clip 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_clip 
dask/array/tests/test_ufunc.py::test_angle 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-1] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_angle 
dask/array/tests/test_ufunc.py::test_issignedinf 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_issignedinf 
dask/array/tests/test_ufunc.py::test_non_ufunc_others[i0] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-index] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_non_ufunc_others[i0] 
dask/array/tests/test_ufunc.py::test_non_ufunc_others[sinc] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_non_ufunc_others[sinc] 
dask/array/tests/test_ufunc.py::test_non_ufunc_others[nan_to_num] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_non_ufunc_others[nan_to_num] 
dask/array/tests/test_ufunc.py::test_frompyfunc 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_frompyfunc 
dask/array/tests/test_ufunc.py::test_frompyfunc_wrapper 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_frompyfunc_wrapper 
dask/array/tests/test_ufunc.py::test_array_ufunc 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_array_ufunc 
dask/array/tests/test_ufunc.py::test_array_ufunc_binop 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_array_ufunc_binop 
dask/array/tests/test_ufunc.py::test_array_ufunc_out 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_array_ufunc_out 
dask/array/tests/test_ufunc.py::test_unsupported_ufunc_methods 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_unsupported_ufunc_methods 
dask/array/tests/test_ufunc.py::test_out_numpy 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_out_numpy 
dask/array/tests/test_ufunc.py::test_out_shape_mismatch 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_out_shape_mismatch 
dask/array/tests/test_ufunc.py::test_divmod 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-0] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_divmod 
dask/array/tests/test_ufunc.py::test_dtype_kwarg[float64] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_dtype_kwarg[float64] 
dask/array/tests/test_ufunc.py::test_dtype_kwarg[float32] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_dtype_kwarg[float32] 
dask/array/tests/test_ufunc.py::test_dtype_kwarg[int32] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_dtype_kwarg[int32] 
dask/array/tests/test_ufunc.py::test_dtype_kwarg[int64] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_dtype_kwarg[int64] 
dask/array/tests/test_ufunc.py::test_ufunc_where[True-False-False-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[True-False-False-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[True-False-False-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[True-False-False-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[True-False-True-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[True-False-True-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[True-False-True-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[True-False-True-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[True-True-False-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[True-True-False-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[True-True-False-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[True-True-False-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[True-True-True-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[True-True-True-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[True-True-True-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[True-True-True-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[False-False-False-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[False-False-False-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[False-False-False-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[False-False-False-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[False-False-True-None] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-columns] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[False-False-True-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[False-False-True-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[False-False-True-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[False-True-False-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[False-True-False-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[False-True-False-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[False-True-False-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[False-True-True-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[False-True-True-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[False-True-True-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[False-True-True-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-False-False-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-False-False-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-False-False-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-False-False-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-False-True-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-False-True-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-False-True-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-False-True-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-True-False-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-True-False-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-True-False-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-True-False-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-True-True-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-True-True-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-True-True-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[numpy-True-True-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[dask-False-False-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[dask-False-False-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[dask-False-False-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[dask-False-False-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[dask-False-True-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[dask-False-True-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[dask-False-True-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[dask-False-True-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[dask-True-False-None] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[dask-True-False-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[dask-True-False-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[dask-True-False-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where[dask-True-True-None] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-1] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[dask-True-True-None] 
dask/array/tests/test_ufunc.py::test_ufunc_where[dask-True-True-f8] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where[dask-True-True-f8] 
dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[False-False-False] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[False-False-False] 
dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[False-False-True] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[False-False-True] 
dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[False-True-False] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[False-True-False] 
dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[False-True-True] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[False-True-True] 
dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[True-False-False] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[True-False-False] 
dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[True-False-True] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[True-False-True] 
dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[True-True-False] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[True-True-False] 
dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[True-True-True] 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where_broadcasts[True-True-True] 
dask/array/tests/test_ufunc.py::test_ufunc_where_no_out 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where_no_out 
dask/array/tests/test_ufunc.py::test_ufunc_where_doesnt_mutate_out 
[gw1] [ 32%] PASSED dask/array/tests/test_ufunc.py::test_ufunc_where_doesnt_mutate_out 
dask/array/tests/test_wrap.py::test_ones 
[gw1] [ 32%] PASSED dask/array/tests/test_wrap.py::test_ones 
dask/array/tests/test_wrap.py::test_size_as_list 
[gw1] [ 32%] PASSED dask/array/tests/test_wrap.py::test_size_as_list 
dask/array/tests/test_wrap.py::test_singleton_size 
[gw1] [ 32%] PASSED dask/array/tests/test_wrap.py::test_singleton_size 
dask/array/tests/test_wrap.py::test_kwargs 
[gw1] [ 32%] PASSED dask/array/tests/test_wrap.py::test_kwargs 
dask/array/tests/test_wrap.py::test_full 
[gw1] [ 32%] PASSED dask/array/tests/test_wrap.py::test_full 
dask/array/tests/test_wrap.py::test_full_error_nonscalar_fill_value 
[gw1] [ 32%] PASSED dask/array/tests/test_wrap.py::test_full_error_nonscalar_fill_value 
dask/array/tests/test_wrap.py::test_full_detects_da_dtype 
[gw1] [ 32%] PASSED dask/array/tests/test_wrap.py::test_full_detects_da_dtype 
dask/array/tests/test_wrap.py::test_full_none_dtype 
[gw1] [ 32%] PASSED dask/array/tests/test_wrap.py::test_full_none_dtype 
dask/array/tests/test_wrap.py::test_full_like_error_nonscalar_fill_value 
[gw1] [ 32%] PASSED dask/array/tests/test_wrap.py::test_full_like_error_nonscalar_fill_value 
dask/array/tests/test_wrap.py::test_can_make_really_big_array_of_ones 
[gw1] [ 32%] PASSED dask/array/tests/test_wrap.py::test_can_make_really_big_array_of_ones 
dask/array/tests/test_wrap.py::test_wrap_consistent_names 
[gw1] [ 32%] PASSED dask/array/tests/test_wrap.py::test_wrap_consistent_names 
dask/bag/tests/test_avro.py::test_onefile_oneblock 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-index] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-0] 
[gw1] [ 32%] PASSED dask/bag/tests/test_avro.py::test_onefile_oneblock 
dask/bag/tests/test_avro.py::test_twofile_oneblock 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-columns] 
[gw1] [ 32%] PASSED dask/bag/tests/test_avro.py::test_twofile_oneblock 
dask/bag/tests/test_avro.py::test_twofile_multiblock 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-1] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-index] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-0] 
[gw1] [ 32%] PASSED dask/bag/tests/test_avro.py::test_twofile_multiblock 
dask/bag/tests/test_avro.py::test_roundtrip_simple 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-columns] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-1] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-index] 
[gw0] [ 32%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-columns] 
[gw1] [ 33%] PASSED dask/bag/tests/test_avro.py::test_roundtrip_simple 
dask/bag/tests/test_avro.py::test_roundtrip[null] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-1] 
[gw1] [ 33%] PASSED dask/bag/tests/test_avro.py::test_roundtrip[null] 
dask/bag/tests/test_avro.py::test_roundtrip[deflate] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-0] 
[gw1] [ 33%] PASSED dask/bag/tests/test_avro.py::test_roundtrip[deflate] 
dask/bag/tests/test_avro.py::test_roundtrip[snappy] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-0] 
[gw1] [ 33%] PASSED dask/bag/tests/test_avro.py::test_roundtrip[snappy] 
dask/bag/tests/test_avro.py::test_invalid_schema 
[gw1] [ 33%] PASSED dask/bag/tests/test_avro.py::test_invalid_schema 
dask/bag/tests/test_bag.py::test_Bag 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_Bag 
dask/bag/tests/test_bag.py::test_keys 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_keys 
dask/bag/tests/test_bag.py::test_bag_groupby_pure_hash 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-1] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_pure_hash 
dask/bag/tests/test_bag.py::test_bag_groupby_normal_hash 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-columns] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_normal_hash 
dask/bag/tests/test_bag.py::test_bag_groupby_none[synchronous-disk] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-0] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_none[synchronous-disk] 
dask/bag/tests/test_bag.py::test_bag_groupby_none[synchronous-tasks] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-columns] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_none[synchronous-tasks] 
dask/bag/tests/test_bag.py::test_bag_groupby_none[processes-disk] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-1] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_none[processes-disk] 
dask/bag/tests/test_bag.py::test_bag_groupby_none[processes-tasks] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-columns] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_none[processes-tasks] 
dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[synchronous-disk-none_field] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-index] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[synchronous-disk-none_field] 
dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[synchronous-disk-no_none_fields] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-1] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[synchronous-disk-no_none_fields] 
dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[synchronous-tasks-none_field] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-0] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[synchronous-tasks-none_field] 
dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[synchronous-tasks-no_none_fields] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-columns] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[synchronous-tasks-no_none_fields] 
dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[processes-disk-none_field] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-1] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[processes-disk-none_field] 
dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[processes-disk-no_none_fields] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-columns] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[processes-disk-no_none_fields] 
dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[processes-tasks-none_field] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-index] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[processes-tasks-none_field] 
dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[processes-tasks-no_none_fields] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-0] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_groupby_dataclass[processes-tasks-no_none_fields] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-columns] 
dask/bag/tests/test_bag.py::test_bag_map 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-1] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_bag_map 
dask/bag/tests/test_bag.py::test_map_method 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-1] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_map_method 
dask/bag/tests/test_bag.py::test_starmap 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-index] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-0] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-columns] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-1] 
[gw0] [ 33%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-index] 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_starmap 
dask/bag/tests/test_bag.py::test_filter 
[gw1] [ 33%] PASSED dask/bag/tests/test_bag.py::test_filter 
dask/bag/tests/test_bag.py::test_remove 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-0] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_remove 
dask/bag/tests/test_bag.py::test_iter 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-index] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_iter 
dask/bag/tests/test_bag.py::test_repr[str] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_repr[str] 
dask/bag/tests/test_bag.py::test_repr[repr] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_repr[repr] 
dask/bag/tests/test_bag.py::test_pluck 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-1] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_pluck 
dask/bag/tests/test_bag.py::test_pluck_with_default 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_pluck_with_default 
dask/bag/tests/test_bag.py::test_unzip 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-0] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_unzip 
dask/bag/tests/test_bag.py::test_fold 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-0] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_fold 
dask/bag/tests/test_bag.py::test_fold_bag 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_fold_bag 
dask/bag/tests/test_bag.py::test_distinct 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_distinct 
dask/bag/tests/test_bag.py::test_distinct_with_key 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_distinct_with_key 
dask/bag/tests/test_bag.py::test_frequencies 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_frequencies 
dask/bag/tests/test_bag.py::test_frequencies_sorted 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-1] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_frequencies_sorted 
dask/bag/tests/test_bag.py::test_topk 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-index] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_topk 
dask/bag/tests/test_bag.py::test_topk_with_non_callable_key[1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_topk_with_non_callable_key[1] 
dask/bag/tests/test_bag.py::test_topk_with_non_callable_key[2] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-0] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_topk_with_non_callable_key[2] 
dask/bag/tests/test_bag.py::test_topk_with_multiarg_lambda 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_topk_with_multiarg_lambda 
dask/bag/tests/test_bag.py::test_lambdas 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-index] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_lambdas 
dask/bag/tests/test_bag.py::test_reductions 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-0] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_reductions 
dask/bag/tests/test_bag.py::test_reduction_names 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_reduction_names 
dask/bag/tests/test_bag.py::test_tree_reductions 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-1] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_tree_reductions 
dask/bag/tests/test_bag.py::test_aggregation[1] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_aggregation[1] 
dask/bag/tests/test_bag.py::test_aggregation[3] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_aggregation[3] 
dask/bag/tests/test_bag.py::test_aggregation[4] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_aggregation[4] 
dask/bag/tests/test_bag.py::test_non_splittable_reductions[1] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_non_splittable_reductions[1] 
dask/bag/tests/test_bag.py::test_non_splittable_reductions[10] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_non_splittable_reductions[10] 
dask/bag/tests/test_bag.py::test_std 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-0] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_std 
dask/bag/tests/test_bag.py::test_var 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_var 
dask/bag/tests/test_bag.py::test_join[identity] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-0] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_join[identity] 
dask/bag/tests/test_bag.py::test_join[delayed] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-1] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_join[delayed] 
dask/bag/tests/test_bag.py::test_join[<lambda>] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_join[<lambda>] 
dask/bag/tests/test_bag.py::test_foldby 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_foldby 
dask/bag/tests/test_bag.py::test_foldby_tree_reduction 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_foldby_tree_reduction 
dask/bag/tests/test_bag.py::test_map_partitions 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-1] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_map_partitions 
dask/bag/tests/test_bag.py::test_map_partitions_args_kwargs 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_map_partitions_args_kwargs 
dask/bag/tests/test_bag.py::test_map_partitions_blockwise 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_map_partitions_blockwise 
dask/bag/tests/test_bag.py::test_random_sample_size 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-0] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_random_sample_size 
dask/bag/tests/test_bag.py::test_random_sample_prob_range 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_random_sample_prob_range 
dask/bag/tests/test_bag.py::test_random_sample_repeated_computation 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-index] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_random_sample_repeated_computation 
dask/bag/tests/test_bag.py::test_random_sample_different_definitions 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_random_sample_different_definitions 
dask/bag/tests/test_bag.py::test_random_sample_random_state 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-0] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_random_sample_random_state 
dask/bag/tests/test_bag.py::test_lazify_task 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_lazify_task 
dask/bag/tests/test_bag.py::test_lazify_task_legacy 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_lazify_task_legacy 
dask/bag/tests/test_bag.py::test_lazify 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_lazify 
dask/bag/tests/test_bag.py::test_take 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-index] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_take 
dask/bag/tests/test_bag.py::test_take_npartitions 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-index] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_take_npartitions 
dask/bag/tests/test_bag.py::test_take_npartitions_warn 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_take_npartitions_warn 
dask/bag/tests/test_bag.py::test_map_is_lazy 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_map_is_lazy 
dask/bag/tests/test_bag.py::test_can_use_dict_to_make_concrete 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_can_use_dict_to_make_concrete 
dask/bag/tests/test_bag.py::test_read_text 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-index] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_read_text 
dask/bag/tests/test_bag.py::test_read_text_large 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-0] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_read_text_large 
dask/bag/tests/test_bag.py::test_read_text_encoding 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-columns] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-index] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_read_text_encoding 
dask/bag/tests/test_bag.py::test_read_text_large_gzip 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-1] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_read_text_large_gzip 
dask/bag/tests/test_bag.py::test_from_sequence 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-index] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_from_sequence 
dask/bag/tests/test_bag.py::test_from_long_sequence 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-0] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-columns] 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_from_long_sequence 
dask/bag/tests/test_bag.py::test_from_empty_sequence 
[gw1] [ 34%] PASSED dask/bag/tests/test_bag.py::test_from_empty_sequence 
dask/bag/tests/test_bag.py::test_product 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-1] 
[gw0] [ 34%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-0] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_product 
dask/bag/tests/test_bag.py::test_partition_collect 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_partition_collect 
dask/bag/tests/test_bag.py::test_groupby 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-columns] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_groupby 
dask/bag/tests/test_bag.py::test_groupby_with_indexer 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_groupby_with_indexer 
dask/bag/tests/test_bag.py::test_groupby_with_npartitions_changed 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_groupby_with_npartitions_changed 
dask/bag/tests/test_bag.py::test_groupby_with_scheduler_func 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-0] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_groupby_with_scheduler_func 
dask/bag/tests/test_bag.py::test_concat 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-columns] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_concat 
dask/bag/tests/test_bag.py::test_flatten 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_flatten 
dask/bag/tests/test_bag.py::test_concat_after_map 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-0] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_concat_after_map 
dask/bag/tests/test_bag.py::test_args 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_args 
dask/bag/tests/test_bag.py::test_to_dataframe 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_to_dataframe 
dask/bag/tests/test_bag.py::test_to_textfiles[gz-GzipFile] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_to_textfiles[gz-GzipFile] 
dask/bag/tests/test_bag.py::test_to_textfiles[bz2-BZ2File] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_to_textfiles[bz2-BZ2File] 
dask/bag/tests/test_bag.py::test_to_textfiles[-open] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_to_textfiles[-open] 
dask/bag/tests/test_bag.py::test_to_textfiles_name_function_preserves_order 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_to_textfiles_name_function_preserves_order 
dask/bag/tests/test_bag.py::test_to_textfiles_name_function_warn 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-0] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_to_textfiles_name_function_warn 
dask/bag/tests/test_bag.py::test_to_textfiles_encoding 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_to_textfiles_encoding 
dask/bag/tests/test_bag.py::test_to_textfiles_inputs 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_to_textfiles_inputs 
dask/bag/tests/test_bag.py::test_to_textfiles_endlines 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_to_textfiles_endlines 
dask/bag/tests/test_bag.py::test_string_namespace 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_string_namespace 
dask/bag/tests/test_bag.py::test_string_namespace_with_unicode 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-columns] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_string_namespace_with_unicode 
dask/bag/tests/test_bag.py::test_str_empty_split 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_str_empty_split 
dask/bag/tests/test_bag.py::test_map_with_iterator_function 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-0] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_map_with_iterator_function 
dask/bag/tests/test_bag.py::test_ensure_compute_output_is_concrete 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_ensure_compute_output_is_concrete 
dask/bag/tests/test_bag.py::test_bag_class_extend 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-columns] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_bag_class_extend 
dask/bag/tests/test_bag.py::test_gh715 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_gh715 
dask/bag/tests/test_bag.py::test_bag_compute_forward_kwargs 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-0] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_bag_compute_forward_kwargs 
dask/bag/tests/test_bag.py::test_to_delayed 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_to_delayed 
dask/bag/tests/test_bag.py::test_to_delayed_optimize_graph 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_to_delayed_optimize_graph 
dask/bag/tests/test_bag.py::test_from_delayed 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_from_delayed 
dask/bag/tests/test_bag.py::test_from_delayed_iterator 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_from_delayed_iterator 
dask/bag/tests/test_bag.py::test_range 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_range 
dask/bag/tests/test_bag.py::test_zip[1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-columns] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_zip[1] 
dask/bag/tests/test_bag.py::test_zip[7] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_zip[7] 
dask/bag/tests/test_bag.py::test_zip[10] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-0] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_zip[10] 
dask/bag/tests/test_bag.py::test_zip[28] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-columns] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_zip[28] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[1-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[1-1] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[1-2] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[1-2] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[1-7] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[1-7] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[1-11] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[1-11] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[1-23] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[1-23] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[2-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[2-1] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[2-2] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[2-2] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-columns] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[2-7] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[2-7] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[2-11] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[2-11] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[2-23] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[2-23] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[5-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[5-1] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[5-2] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[5-2] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[5-7] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[5-7] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[5-11] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[5-11] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[5-23] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[5-23] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[12-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[12-1] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[12-2] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[12-2] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[12-7] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[12-7] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[12-11] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[12-11] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[12-23] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[12-23] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[23-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[23-1] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[23-2] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[23-2] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[23-7] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[23-7] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[23-11] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[23-11] 
dask/bag/tests/test_bag.py::test_repartition_npartitions[23-23] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_npartitions[23-23] 
dask/bag/tests/test_bag.py::test_repartition_partition_size[1-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_partition_size[1-1] 
dask/bag/tests/test_bag.py::test_repartition_partition_size[2-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-columns] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_partition_size[2-1] 
dask/bag/tests/test_bag.py::test_repartition_partition_size[5-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-0] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_partition_size[5-1] 
dask/bag/tests/test_bag.py::test_repartition_partition_size[1-2] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_partition_size[1-2] 
dask/bag/tests/test_bag.py::test_repartition_partition_size[2-2] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-0] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_partition_size[2-2] 
dask/bag/tests/test_bag.py::test_repartition_partition_size[5-2] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_partition_size[5-2] 
dask/bag/tests/test_bag.py::test_repartition_partition_size[1-5] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-columns] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_partition_size[1-5] 
dask/bag/tests/test_bag.py::test_repartition_partition_size[2-5] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-index] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_partition_size[2-5] 
dask/bag/tests/test_bag.py::test_repartition_partition_size[5-5] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-1] 
[gw1] [ 35%] PASSED dask/bag/tests/test_bag.py::test_repartition_partition_size[5-5] 
dask/bag/tests/test_bag.py::test_multiple_repartition_partition_size 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-index] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-0] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-columns] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-1] 
[gw0] [ 35%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-index] 
[gw1] [ 35%] FAILED dask/bag/tests/test_bag.py::test_multiple_repartition_partition_size 
dask/bag/tests/test_bag.py::test_repartition_partition_size_complex_dtypes 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-index] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_repartition_partition_size_complex_dtypes 
dask/bag/tests/test_bag.py::test_repartition_names 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_repartition_names 
dask/bag/tests/test_bag.py::test_repartition_input_errors 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_repartition_input_errors 
dask/bag/tests/test_bag.py::test_accumulate 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-columns] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_accumulate 
dask/bag/tests/test_bag.py::test_groupby_tasks 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-index] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_groupby_tasks 
dask/bag/tests/test_bag.py::test_groupby_tasks_names 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_groupby_tasks_names 
dask/bag/tests/test_bag.py::test_groupby_tasks_2[1000-20-100] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_groupby_tasks_2[1000-20-100] 
dask/bag/tests/test_bag.py::test_groupby_tasks_2[12345-234-1042] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-1] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_groupby_tasks_2[12345-234-1042] 
dask/bag/tests/test_bag.py::test_groupby_tasks_2[100-1-50] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_groupby_tasks_2[100-1-50] 
dask/bag/tests/test_bag.py::test_groupby_tasks_3 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_groupby_tasks_3 
dask/bag/tests/test_bag.py::test_to_textfiles_empty_partitions 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-0] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_to_textfiles_empty_partitions 
dask/bag/tests/test_bag.py::test_reduction_empty 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_reduction_empty 
dask/bag/tests/test_bag.py::test_reduction_empty_aggregate[1] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_reduction_empty_aggregate[1] 
dask/bag/tests/test_bag.py::test_reduction_empty_aggregate[2] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_reduction_empty_aggregate[2] 
dask/bag/tests/test_bag.py::test_reduction_empty_aggregate[4] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_reduction_empty_aggregate[4] 
dask/bag/tests/test_bag.py::test_reduction_with_non_comparable_objects 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_reduction_with_non_comparable_objects 
dask/bag/tests/test_bag.py::test_reduction_with_sparse_matrices[array] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_reduction_with_sparse_matrices[array] 
dask/bag/tests/test_bag.py::test_reduction_with_sparse_matrices[matrix] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_reduction_with_sparse_matrices[matrix] 
dask/bag/tests/test_bag.py::test_empty 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-1] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_empty 
dask/bag/tests/test_bag.py::test_bag_picklable 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-columns] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_bag_picklable 
dask/bag/tests/test_bag.py::test_msgpack_unicode 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_msgpack_unicode 
dask/bag/tests/test_bag.py::test_bag_with_single_callable 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_bag_with_single_callable 
dask/bag/tests/test_bag.py::test_optimize_fuse_keys 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_optimize_fuse_keys 
dask/bag/tests/test_bag.py::test_reductions_are_lazy 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_reductions_are_lazy 
dask/bag/tests/test_bag.py::test_repeated_groupby 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-0] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_repeated_groupby 
dask/bag/tests/test_bag.py::test_temporary_directory 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-columns] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_temporary_directory 
dask/bag/tests/test_bag.py::test_empty_bag 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_empty_bag 
dask/bag/tests/test_bag.py::test_bag_paths 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-1] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_bag_paths 
dask/bag/tests/test_bag.py::test_map_partitions_arg 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_map_partitions_arg 
dask/bag/tests/test_bag.py::test_map_keynames 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_map_keynames 
dask/bag/tests/test_bag.py::test_map_releases_element_references_as_soon_as_possible 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_map_releases_element_references_as_soon_as_possible 
dask/bag/tests/test_bag.py::test_bagged_array_delayed 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-1] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_bagged_array_delayed 
dask/bag/tests/test_bag.py::test_dask_layers 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_dask_layers 
dask/bag/tests/test_bag.py::test_dask_layers_to_delayed[False] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_dask_layers_to_delayed[False] 
dask/bag/tests/test_bag.py::test_dask_layers_to_delayed[True] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_dask_layers_to_delayed[True] 
dask/bag/tests/test_bag.py::test_to_dataframe_optimize_graph 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-columns] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_to_dataframe_optimize_graph 
dask/bag/tests/test_bag.py::test_default_partitioning_worker_saturation[100] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_default_partitioning_worker_saturation[100] 
dask/bag/tests/test_bag.py::test_default_partitioning_worker_saturation[250] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_default_partitioning_worker_saturation[250] 
dask/bag/tests/test_bag.py::test_default_partitioning_worker_saturation[500] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_default_partitioning_worker_saturation[500] 
dask/bag/tests/test_bag.py::test_default_partitioning_worker_saturation[1000] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-1] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_default_partitioning_worker_saturation[1000] 
dask/bag/tests/test_bag.py::test_npartitions_saturation[100] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_npartitions_saturation[100] 
dask/bag/tests/test_bag.py::test_npartitions_saturation[250] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_npartitions_saturation[250] 
dask/bag/tests/test_bag.py::test_npartitions_saturation[500] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_npartitions_saturation[500] 
dask/bag/tests/test_bag.py::test_npartitions_saturation[1000] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-index] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_npartitions_saturation[1000] 
dask/bag/tests/test_bag.py::test_map_total_mem_usage 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-1] 
[gw1] [ 36%] PASSED dask/bag/tests/test_bag.py::test_map_total_mem_usage 
dask/bag/tests/test_random.py::test_choices_size_exactly_k 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-0] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_choices_size_exactly_k 
dask/bag/tests/test_random.py::test_choices_k_bigger_than_bag_size 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-columns] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_choices_k_bigger_than_bag_size 
dask/bag/tests/test_random.py::test_choices_empty_partition 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-0] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_choices_empty_partition 
dask/bag/tests/test_random.py::test_choices_k_bigger_than_smallest_partition_size 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-index] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_choices_k_bigger_than_smallest_partition_size 
dask/bag/tests/test_random.py::test_choices_k_equal_bag_size_with_unbalanced_partitions 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-1] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_choices_k_equal_bag_size_with_unbalanced_partitions 
dask/bag/tests/test_random.py::test_choices_with_more_bag_partitons 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-0] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_choices_with_more_bag_partitons 
dask/bag/tests/test_random.py::test_sample_with_more_bag_partitons 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-index] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_sample_with_more_bag_partitons 
dask/bag/tests/test_random.py::test_sample_size_exactly_k 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-1] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_sample_size_exactly_k 
dask/bag/tests/test_random.py::test_sample_k_bigger_than_bag_size 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-index] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_sample_k_bigger_than_bag_size 
dask/bag/tests/test_random.py::test_sample_empty_partition 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-columns] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-1] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_sample_empty_partition 
dask/bag/tests/test_random.py::test_sample_size_k_bigger_than_smallest_partition_size 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-0] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-columns] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_sample_size_k_bigger_than_smallest_partition_size 
dask/bag/tests/test_random.py::test_sample_k_equal_bag_size_with_unbalanced_partitions 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-0] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_sample_k_equal_bag_size_with_unbalanced_partitions 
dask/bag/tests/test_random.py::test_sample_k_larger_than_partitions 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-columns] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_sample_k_larger_than_partitions 
dask/bag/tests/test_random.py::test_weighted_sampling_without_replacement 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_weighted_sampling_without_replacement 
dask/bag/tests/test_random.py::test_sample_return_bag 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_sample_return_bag 
dask/bag/tests/test_random.py::test_partitions_are_coerced_to_lists 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-1] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-index] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-0] 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_partitions_are_coerced_to_lists 
dask/bag/tests/test_random.py::test_reservoir_sample_map_partitions_correctness 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_reservoir_sample_map_partitions_correctness 
dask/bag/tests/test_random.py::test_reservoir_sample_with_replacement_map_partitions_correctness 
[gw1] [ 36%] PASSED dask/bag/tests/test_random.py::test_reservoir_sample_with_replacement_map_partitions_correctness 
dask/bag/tests/test_text.py::test_read_text[None-None-ascii-True] 
[gw1] [ 36%] PASSED dask/bag/tests/test_text.py::test_read_text[None-None-ascii-True] 
dask/bag/tests/test_text.py::test_read_text[None-None-ascii-False] 
[gw1] [ 36%] PASSED dask/bag/tests/test_text.py::test_read_text[None-None-ascii-False] 
dask/bag/tests/test_text.py::test_read_text[None-None-utf-8-True] 
[gw1] [ 36%] PASSED dask/bag/tests/test_text.py::test_read_text[None-None-utf-8-True] 
dask/bag/tests/test_text.py::test_read_text[None-None-utf-8-False] 
[gw1] [ 36%] PASSED dask/bag/tests/test_text.py::test_read_text[None-None-utf-8-False] 
dask/bag/tests/test_text.py::test_read_text[zip-None-ascii-True] 
[gw1] [ 36%] PASSED dask/bag/tests/test_text.py::test_read_text[zip-None-ascii-True] 
dask/bag/tests/test_text.py::test_read_text[zip-None-ascii-False] 
[gw1] [ 36%] PASSED dask/bag/tests/test_text.py::test_read_text[zip-None-ascii-False] 
dask/bag/tests/test_text.py::test_read_text[zip-None-utf-8-True] 
[gw0] [ 36%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-columns] 
[gw1] [ 36%] PASSED dask/bag/tests/test_text.py::test_read_text[zip-None-utf-8-True] 
dask/bag/tests/test_text.py::test_read_text[zip-None-utf-8-False] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[zip-None-utf-8-False] 
dask/bag/tests/test_text.py::test_read_text[bz2-None-ascii-True] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[bz2-None-ascii-True] 
dask/bag/tests/test_text.py::test_read_text[bz2-None-ascii-False] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[bz2-None-ascii-False] 
dask/bag/tests/test_text.py::test_read_text[bz2-None-utf-8-True] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[bz2-None-utf-8-True] 
dask/bag/tests/test_text.py::test_read_text[bz2-None-utf-8-False] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[bz2-None-utf-8-False] 
dask/bag/tests/test_text.py::test_read_text[gzip-None-ascii-True] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[gzip-None-ascii-True] 
dask/bag/tests/test_text.py::test_read_text[gzip-None-ascii-False] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[gzip-None-ascii-False] 
dask/bag/tests/test_text.py::test_read_text[gzip-None-utf-8-True] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[gzip-None-utf-8-True] 
dask/bag/tests/test_text.py::test_read_text[gzip-None-utf-8-False] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[gzip-None-utf-8-False] 
dask/bag/tests/test_text.py::test_read_text[lzma-None-ascii-True] 
[gw1] [ 37%] SKIPPED dask/bag/tests/test_text.py::test_read_text[lzma-None-ascii-True] 
dask/bag/tests/test_text.py::test_read_text[lzma-None-ascii-False] 
[gw1] [ 37%] SKIPPED dask/bag/tests/test_text.py::test_read_text[lzma-None-ascii-False] 
dask/bag/tests/test_text.py::test_read_text[lzma-None-utf-8-True] 
[gw1] [ 37%] SKIPPED dask/bag/tests/test_text.py::test_read_text[lzma-None-utf-8-True] 
dask/bag/tests/test_text.py::test_read_text[lzma-None-utf-8-False] 
[gw1] [ 37%] SKIPPED dask/bag/tests/test_text.py::test_read_text[lzma-None-utf-8-False] 
dask/bag/tests/test_text.py::test_read_text[xz-None-ascii-True] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[xz-None-ascii-True] 
dask/bag/tests/test_text.py::test_read_text[xz-None-ascii-False] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[xz-None-ascii-False] 
dask/bag/tests/test_text.py::test_read_text[xz-None-utf-8-True] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[xz-None-utf-8-True] 
dask/bag/tests/test_text.py::test_read_text[xz-None-utf-8-False] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[xz-None-utf-8-False] 
dask/bag/tests/test_text.py::test_read_text[snappy-None-ascii-True] 
[gw1] [ 37%] SKIPPED dask/bag/tests/test_text.py::test_read_text[snappy-None-ascii-True] 
dask/bag/tests/test_text.py::test_read_text[snappy-None-ascii-False] 
[gw1] [ 37%] SKIPPED dask/bag/tests/test_text.py::test_read_text[snappy-None-ascii-False] 
dask/bag/tests/test_text.py::test_read_text[snappy-None-utf-8-True] 
[gw1] [ 37%] SKIPPED dask/bag/tests/test_text.py::test_read_text[snappy-None-utf-8-True] 
dask/bag/tests/test_text.py::test_read_text[snappy-None-utf-8-False] 
[gw1] [ 37%] SKIPPED dask/bag/tests/test_text.py::test_read_text[snappy-None-utf-8-False] 
dask/bag/tests/test_text.py::test_read_text[None-10 B-ascii-True] 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-1] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[None-10 B-ascii-True] 
dask/bag/tests/test_text.py::test_read_text[None-10 B-ascii-False] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[None-10 B-ascii-False] 
dask/bag/tests/test_text.py::test_read_text[None-10 B-utf-8-True] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[None-10 B-utf-8-True] 
dask/bag/tests/test_text.py::test_read_text[None-10 B-utf-8-False] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text[None-10 B-utf-8-False] 
dask/bag/tests/test_text.py::test_read_text_unicode_no_collection 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_read_text_unicode_no_collection 
dask/bag/tests/test_text.py::test_files_per_partition 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-index] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_files_per_partition 
dask/bag/tests/test_text.py::test_errors 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-0] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_errors 
dask/bag/tests/test_text.py::test_complex_delimiter 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-columns] 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-1] 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-index] 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-0] 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-columns] 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-1] 
[gw1] [ 37%] PASSED dask/bag/tests/test_text.py::test_complex_delimiter 
dask/bytes/tests/test_bytes_utils.py::test_read_block 
[gw1] [ 37%] PASSED dask/bytes/tests/test_bytes_utils.py::test_read_block 
dask/bytes/tests/test_bytes_utils.py::test_seek_delimiter_endline 
[gw1] [ 37%] PASSED dask/bytes/tests/test_bytes_utils.py::test_seek_delimiter_endline 
dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options 
[gw1] [ 37%] PASSED dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options 
dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[c:\\foo\\bar-c:\\foo\\bar] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[c:\\foo\\bar-c:\\foo\\bar] 
dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[C:\\\\foo\\bar-C:\\\\foo\\bar] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[C:\\\\foo\\bar-C:\\\\foo\\bar] 
dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[c:/foo/bar-c:/foo/bar] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[c:/foo/bar-c:/foo/bar] 
dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[file:///c|\\foo\\bar-c:\\foo\\bar] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[file:///c|\\foo\\bar-c:\\foo\\bar] 
dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[file:///C|/foo/bar-C:/foo/bar] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[file:///C|/foo/bar-C:/foo/bar] 
dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[file:///C:/foo/bar-C:/foo/bar] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_bytes_utils.py::test_infer_storage_options_c[file:///C:/foo/bar-C:/foo/bar] 
dask/bytes/tests/test_bytes_utils.py::test_stringify_path 
[gw1] [ 37%] PASSED dask/bytes/tests/test_bytes_utils.py::test_stringify_path 
dask/bytes/tests/test_compression.py::test_files[None-noop_file] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_compression.py::test_files[None-noop_file] 
dask/bytes/tests/test_compression.py::test_files[zip-unzip] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_compression.py::test_files[zip-unzip] 
dask/bytes/tests/test_compression.py::test_files[bz2-BZ2File] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_compression.py::test_files[bz2-BZ2File] 
dask/bytes/tests/test_compression.py::test_files[gzip-<lambda>] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_compression.py::test_files[gzip-<lambda>] 
dask/bytes/tests/test_compression.py::test_files[lzma-LZMAFile] 
[gw1] [ 37%] SKIPPED dask/bytes/tests/test_compression.py::test_files[lzma-LZMAFile] 
dask/bytes/tests/test_compression.py::test_files[xz-LZMAFile] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_compression.py::test_files[xz-LZMAFile] 
dask/bytes/tests/test_compression.py::test_files[snappy-SnappyFile] 
[gw1] [ 37%] SKIPPED dask/bytes/tests/test_compression.py::test_files[snappy-SnappyFile] 
dask/bytes/tests/test_http.py::test_simple 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-index] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_http.py::test_simple 
dask/bytes/tests/test_http.py::test_loc 
[gw1] [ 37%] PASSED dask/bytes/tests/test_http.py::test_loc 
dask/bytes/tests/test_http.py::test_fetch_range_with_headers 
[gw1] [ 37%] PASSED dask/bytes/tests/test_http.py::test_fetch_range_with_headers 
dask/bytes/tests/test_http.py::test_ops[None] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_http.py::test_ops[None] 
dask/bytes/tests/test_http.py::test_ops[99999] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_http.py::test_ops[99999] 
dask/bytes/tests/test_http.py::test_ops_blocksize 
[gw1] [ 37%] PASSED dask/bytes/tests/test_http.py::test_ops_blocksize 
dask/bytes/tests/test_http.py::test_errors 
[gw1] [ 37%] PASSED dask/bytes/tests/test_http.py::test_errors 
dask/bytes/tests/test_http.py::test_files 
[gw1] [ 37%] PASSED dask/bytes/tests/test_http.py::test_files 
dask/bytes/tests/test_http.py::test_open_glob 
[gw1] [ 37%] PASSED dask/bytes/tests/test_http.py::test_open_glob 
dask/bytes/tests/test_local.py::test_unordered_urlpath_errors 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_unordered_urlpath_errors 
dask/bytes/tests/test_local.py::test_read_bytes 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_read_bytes 
dask/bytes/tests/test_local.py::test_read_bytes_sample_delimiter 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_read_bytes_sample_delimiter 
dask/bytes/tests/test_local.py::test_parse_sample_bytes 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_parse_sample_bytes 
dask/bytes/tests/test_local.py::test_read_bytes_no_sample 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_read_bytes_no_sample 
dask/bytes/tests/test_local.py::test_read_bytes_blocksize_none 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_read_bytes_blocksize_none 
dask/bytes/tests/test_local.py::test_read_bytes_blocksize_types[5.0] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_read_bytes_blocksize_types[5.0] 
dask/bytes/tests/test_local.py::test_read_bytes_blocksize_types[5 B] 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-0] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_read_bytes_blocksize_types[5 B] 
dask/bytes/tests/test_local.py::test_read_bytes_blocksize_float_errs 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_read_bytes_blocksize_float_errs 
dask/bytes/tests/test_local.py::test_read_bytes_include_path 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_read_bytes_include_path 
dask/bytes/tests/test_local.py::test_with_urls 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_with_urls 
dask/bytes/tests/test_local.py::test_with_paths 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_with_paths 
dask/bytes/tests/test_local.py::test_read_bytes_block 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_read_bytes_block 
dask/bytes/tests/test_local.py::test_read_bytes_delimited 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_read_bytes_delimited 
dask/bytes/tests/test_local.py::test_compression[None-None] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_compression[None-None] 
dask/bytes/tests/test_local.py::test_compression[zip-None] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_compression[zip-None] 
dask/bytes/tests/test_local.py::test_compression[bz2-None] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_compression[bz2-None] 
dask/bytes/tests/test_local.py::test_compression[gzip-None] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_compression[gzip-None] 
dask/bytes/tests/test_local.py::test_compression[lzma-None] 
[gw1] [ 37%] SKIPPED dask/bytes/tests/test_local.py::test_compression[lzma-None] 
dask/bytes/tests/test_local.py::test_compression[xz-None] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_compression[xz-None] 
dask/bytes/tests/test_local.py::test_compression[snappy-None] 
[gw1] [ 37%] SKIPPED dask/bytes/tests/test_local.py::test_compression[snappy-None] 
dask/bytes/tests/test_local.py::test_compression[None-10] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_compression[None-10] 
dask/bytes/tests/test_local.py::test_compression[zip-10] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_compression[zip-10] 
dask/bytes/tests/test_local.py::test_compression[bz2-10] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_compression[bz2-10] 
dask/bytes/tests/test_local.py::test_compression[gzip-10] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_compression[gzip-10] 
dask/bytes/tests/test_local.py::test_compression[lzma-10] 
[gw1] [ 37%] SKIPPED dask/bytes/tests/test_local.py::test_compression[lzma-10] 
dask/bytes/tests/test_local.py::test_compression[xz-10] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_compression[xz-10] 
dask/bytes/tests/test_local.py::test_compression[snappy-10] 
[gw1] [ 37%] SKIPPED dask/bytes/tests/test_local.py::test_compression[snappy-10] 
dask/bytes/tests/test_local.py::test_open_files 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files 
dask/bytes/tests/test_local.py::test_open_files_text_mode[utf-8] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_text_mode[utf-8] 
dask/bytes/tests/test_local.py::test_open_files_text_mode[ascii] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_text_mode[ascii] 
dask/bytes/tests/test_local.py::test_open_files_compression[None-rt] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_compression[None-rt] 
dask/bytes/tests/test_local.py::test_open_files_compression[None-rb] 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-columns] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_compression[None-rb] 
dask/bytes/tests/test_local.py::test_open_files_compression[zip-rt] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_compression[zip-rt] 
dask/bytes/tests/test_local.py::test_open_files_compression[zip-rb] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_compression[zip-rb] 
dask/bytes/tests/test_local.py::test_open_files_compression[bz2-rt] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_compression[bz2-rt] 
dask/bytes/tests/test_local.py::test_open_files_compression[bz2-rb] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_compression[bz2-rb] 
dask/bytes/tests/test_local.py::test_open_files_compression[gzip-rt] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_compression[gzip-rt] 
dask/bytes/tests/test_local.py::test_open_files_compression[gzip-rb] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_compression[gzip-rb] 
dask/bytes/tests/test_local.py::test_open_files_compression[lzma-rt] 
[gw1] [ 37%] SKIPPED dask/bytes/tests/test_local.py::test_open_files_compression[lzma-rt] 
dask/bytes/tests/test_local.py::test_open_files_compression[lzma-rb] 
[gw1] [ 37%] SKIPPED dask/bytes/tests/test_local.py::test_open_files_compression[lzma-rb] 
dask/bytes/tests/test_local.py::test_open_files_compression[xz-rt] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_compression[xz-rt] 
dask/bytes/tests/test_local.py::test_open_files_compression[xz-rb] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_compression[xz-rb] 
dask/bytes/tests/test_local.py::test_open_files_compression[snappy-rt] 
[gw1] [ 37%] SKIPPED dask/bytes/tests/test_local.py::test_open_files_compression[snappy-rt] 
dask/bytes/tests/test_local.py::test_open_files_compression[snappy-rb] 
[gw1] [ 37%] SKIPPED dask/bytes/tests/test_local.py::test_open_files_compression[snappy-rb] 
dask/bytes/tests/test_local.py::test_bad_compression 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_bad_compression 
dask/bytes/tests/test_local.py::test_not_found 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_not_found 
dask/bytes/tests/test_local.py::test_names 
[gw1] [ 37%] SKIPPED dask/bytes/tests/test_local.py::test_names 
dask/bytes/tests/test_local.py::test_open_files_write[compression_opener0] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_write[compression_opener0] 
dask/bytes/tests/test_local.py::test_open_files_write[compression_opener1] 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_open_files_write[compression_opener1] 
dask/bytes/tests/test_local.py::test_pickability_of_lazy_files 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_pickability_of_lazy_files 
dask/bytes/tests/test_local.py::test_py2_local_bytes 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_py2_local_bytes 
dask/bytes/tests/test_local.py::test_abs_paths 
[gw1] [ 37%] PASSED dask/bytes/tests/test_local.py::test_abs_paths 
dask/dataframe/dask_expr/io/tests/test_delayed.py::test_from_delayed_optimizing 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_delayed.py::test_from_delayed_optimizing 
dask/dataframe/dask_expr/io/tests/test_delayed.py::test_from_delayed[None] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_delayed.py::test_from_delayed[None] 
dask/dataframe/dask_expr/io/tests/test_delayed.py::test_from_delayed[foo] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_delayed.py::test_from_delayed[foo] 
dask/dataframe/dask_expr/io/tests/test_delayed.py::test_from_delayed_dask 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-1] 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-index] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_delayed.py::test_from_delayed_dask 
dask/dataframe/dask_expr/io/tests/test_delayed.py::test_from_delayed_fusion 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_delayed.py::test_from_delayed_fusion 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas[Series-True] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas[Series-True] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas[Series-False] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas[Series-False] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas[DataFrame-True] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas[DataFrame-True] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas[DataFrame-False] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas[DataFrame-False] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_noargs[Series] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_noargs[Series] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_noargs[DataFrame] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_noargs[DataFrame] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_empty[Series] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_empty[Series] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_empty[DataFrame] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_empty[DataFrame] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_immutable[Series] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_immutable[Series] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_immutable[DataFrame] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_immutable[DataFrame] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_sort_and_different_partitions 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_sort_and_different_partitions 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_sort 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_sort 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_divisions 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-0] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_divisions 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_empty_projection 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_empty_projection 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_divisions_duplicated 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_divisions_duplicated 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-True-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-True-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-True-3] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-True-3] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-True-6] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-True-6] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-True-7] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-True-7] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-False-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-False-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-False-3] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-False-3] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-False-6] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-False-6] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-False-7] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[Series-False-7] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-True-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-True-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-True-3] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-True-3] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-True-6] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-True-6] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-True-7] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-True-7] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-False-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-False-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-False-3] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-False-3] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-False-6] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-False-6] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-False-7] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions[DataFrame-False-7] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-True-1-6] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-True-1-6] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-True-2-3] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-True-2-3] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-True-6-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-True-6-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-True-7-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-True-7-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-False-1-6] 
[gw0] [ 37%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-0] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-False-1-6] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-columns] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-False-2-3] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-False-2-3] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-False-6-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-False-6-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-False-7-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[Series-False-7-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-True-1-6] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-True-1-6] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-True-2-3] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-True-2-3] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-True-6-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-True-6-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-True-7-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-True-7-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-False-1-6] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-False-1-6] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-False-2-3] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-False-2-3] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-False-6-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-False-6-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-False-7-1] 
[gw1] [ 37%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_chunksize[DataFrame-False-7-1] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions_and_chunksize[Series] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions_and_chunksize[Series] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions_and_chunksize[DataFrame] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_npartitions_and_chunksize[DataFrame] 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_string_option 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_string_option 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_deepcopy 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_deepcopy 
dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_empty_chunksize 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_from_pandas.py::test_from_pandas_empty_chunksize 
dask/dataframe/dask_expr/io/tests/test_io.py::test_simplify[<lambda>-<lambda>0] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-1] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_simplify[<lambda>-<lambda>0] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_simplify[<lambda>-<lambda>1] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_simplify[<lambda>-<lambda>1] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_simplify[<lambda>-<lambda>2] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_simplify[<lambda>-<lambda>2] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_simplify[<lambda>-<lambda>3] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_simplify[<lambda>-<lambda>3] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_simplify[<lambda>-<lambda>4] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_simplify[<lambda>-<lambda>4] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_io_fusion[parquet] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_io_fusion[parquet] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_io_fusion[csv] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_io_fusion[csv] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_csv_integer_usecols 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_csv_integer_usecols 
dask/dataframe/dask_expr/io/tests/test_io.py::test_read_csv_keywords 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_read_csv_keywords 
dask/dataframe/dask_expr/io/tests/test_io.py::test_io_fusion_blockwise 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-index] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_io_fusion_blockwise 
dask/dataframe/dask_expr/io/tests/test_io.py::test_repartition_io_fusion_blockwise 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_repartition_io_fusion_blockwise 
dask/dataframe/dask_expr/io/tests/test_io.py::test_io_fusion_merge 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-0] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_io_fusion_merge 
dask/dataframe/dask_expr/io/tests/test_io.py::test_io_fusion_zero 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_io_fusion_zero 
dask/dataframe/dask_expr/io/tests/test_io.py::test_io_culling[parquet] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-columns] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_io_culling[parquet] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_io_culling[csv] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_io_culling[csv] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_io_culling[pandas] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_io_culling[pandas] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_parquet_complex_filters 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_parquet_complex_filters 
dask/dataframe/dask_expr/io/tests/test_io.py::test_to_dask_array[True] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_to_dask_array[True] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_to_dask_array[False] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_to_dask_array[False] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_combine_similar[parquet-read_parquet-ReadParquet] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-1] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-index] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/io/tests/test_io.py::test_combine_similar[parquet-read_parquet-ReadParquet] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_combine_similar[csv-read_csv-FromMap] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-0] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-columns] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/io/tests/test_io.py::test_combine_similar[csv-read_csv-FromMap] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_combine_similar_no_projection_on_one_branch 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-1] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-index] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/io/tests/test_io.py::test_combine_similar_no_projection_on_one_branch 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-True-None-True] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-True-None-True] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-True-None-False] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-0] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-True-None-False] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-True-foo-True] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-columns] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-True-foo-True] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-True-foo-False] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-True-foo-False] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-False-None-True] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-1] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-False-None-True] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-False-None-False] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-index] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-False-None-False] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-False-foo-True] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-False-foo-True] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-False-foo-False] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-0] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[True-False-foo-False] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-True-None-True] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-True-None-True] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-True-None-False] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-columns] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-True-None-False] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-True-foo-True] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-True-foo-True] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-True-foo-False] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-1] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-True-foo-False] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-False-None-True] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-False-None-True] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-False-None-False] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-index] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-False-None-False] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-False-foo-True] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-False-foo-True] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-False-foo-False] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-0] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map[False-False-foo-False] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map_columns_required 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map_columns_required 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map_string_conversion 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_map_string_conversion 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_array 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-columns] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_array 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_array_string_conersion[object] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_array_string_conersion[object] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_array_string_conersion[str] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_array_string_conersion[str] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_dask_array 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_dask_array 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_dask_array_scalar_columns[1] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_dask_array_scalar_columns[1] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_dask_array_scalar_columns[aa] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_dask_array_scalar_columns[aa] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_dask_array_projection 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_dask_array_projection 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_dict 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-1] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_dict 
dask/dataframe/dask_expr/io/tests/test_io.py::test_normalize_token_parquet_filemetadata_and_schema[filemetadata] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_normalize_token_parquet_filemetadata_and_schema[filemetadata] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_normalize_token_parquet_filemetadata_and_schema[schema] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_normalize_token_parquet_filemetadata_and_schema[schema] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_to_records_with_lengths[lengths0] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_to_records_with_lengths[lengths0] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_to_records_with_lengths[True] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_to_records_with_lengths[True] 
dask/dataframe/dask_expr/io/tests/test_io.py::test_to_bag 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-index] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-0] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-columns] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-1] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-index] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-0] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-columns] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-1] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-index] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-0] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-columns] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-1] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-index] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-0] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-columns] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-1] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-index] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-0] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-columns] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-1] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_to_bag 
dask/dataframe/dask_expr/io/tests/test_io.py::test_from_array_partition_pruning 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_io.py::test_from_array_partition_pruning 
dask/dataframe/dask_expr/io/tests/test_io.py::test_map_partitions_assign_fusedio 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-index] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-0] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/io/tests/test_io.py::test_map_partitions_assign_fusedio 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_len[arrow] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_len[arrow] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_missing_stats[arrow] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_missing_stats[arrow] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_all_na_column[arrow-nan] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_all_na_column[arrow-nan] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_all_na_column[arrow-1] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_all_na_column[arrow-1] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_len_filter[arrow] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_len_filter[arrow] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_len_empty_dir 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_parquet_len_empty_dir 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_to_parquet[True] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-columns] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-1] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_to_parquet[True] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_to_parquet[False] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-index] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-0] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_to_parquet[False] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_dtype_backend[pyarrow] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_dtype_backend[pyarrow] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_dtype_backend[numpy_nullable] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_dtype_backend[numpy_nullable] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_dtype_backend[None] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_dtype_backend[None] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_types_mapper[None] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-columns] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_types_mapper[None] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_types_mapper[<lambda>] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_types_mapper[<lambda>] 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_serialize 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_serialize 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_filters 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_filters 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_list_of_files 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_pyarrow_filesystem_list_of_files 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_partition_pruning 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_partition_pruning 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_predicate_pushdown 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-1] 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_predicate_pushdown 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_predicate_pushdown_compound 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_predicate_pushdown_compound 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_aggregate_rg_stats_to_file 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_aggregate_rg_stats_to_file 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_aggregate_statistics_to_file 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_aggregate_statistics_to_file 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_combine_statistics 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_combine_statistics 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_index_only_from_parquet 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_index_only_from_parquet 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_timestamp_divisions 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-index] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_timestamp_divisions 
dask/dataframe/dask_expr/io/tests/test_parquet.py::test_read_parquet_index_projection 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-0] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-columns] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-1] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_read_parquet_index_projection 
dask/dataframe/dask_expr/io/tests/test_sql.py::test_shuffle_after_read_sql 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/io/tests/test_sql.py::test_shuffle_after_read_sql 
dask/dataframe/dask_expr/tests/test_align_partitions.py::test_broadcasting_scalar[__add__] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-index] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_align_partitions.py::test_broadcasting_scalar[__add__] 
dask/dataframe/dask_expr/tests/test_align_partitions.py::test_broadcasting_scalar[add] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-0] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_align_partitions.py::test_broadcasting_scalar[add] 
dask/dataframe/dask_expr/tests/test_align_partitions.py::test_assign_align_partitions 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-columns] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-1] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/tests/test_align_partitions.py::test_assign_align_partitions 
dask/dataframe/dask_expr/tests/test_align_partitions.py::test_assign_unknown_partitions 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_align_partitions.py::test_assign_unknown_partitions 
dask/dataframe/dask_expr/tests/test_categorical.py::test_set_categories 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/tests/test_categorical.py::test_set_categories 
dask/dataframe/dask_expr/tests/test_categorical.py::test_categorize 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-index] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/tests/test_categorical.py::test_categorize 
dask/dataframe/dask_expr/tests/test_categorical.py::test_get_categories_simplify_adds_projection 
[gw1] [ 38%] PASSED dask/dataframe/dask_expr/tests/test_categorical.py::test_get_categories_simplify_adds_projection 
dask/dataframe/dask_expr/tests/test_categorical.py::test_categorical_set_index 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-0] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/tests/test_categorical.py::test_categorical_set_index 
dask/dataframe/dask_expr/tests/test_categorical.py::test_categorize_drops_category_columns 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-columns] 
[gw1] [ 38%] FAILED dask/dataframe/dask_expr/tests/test_categorical.py::test_categorize_drops_category_columns 
dask/dataframe/dask_expr/tests/test_collection.py::test_del 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-1] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_del 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-None-True] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-index] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-None-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-None-False] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-0] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-None-False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-None-None] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-columns] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-None-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-StringIO-True] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-1] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-StringIO-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-StringIO-False] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-index] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-StringIO-False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-StringIO-None] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-0] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-StringIO-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-None-True] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-columns] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-None-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-None-False] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-1] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-None-False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-None-None] 
[gw0] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-index] 
[gw1] [ 38%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-None-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-StringIO-True] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-StringIO-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-StringIO-False] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-StringIO-False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-StringIO-None] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-StringIO-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-None-True] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-None-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-None-False] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-None-False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-None-None] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-None-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-StringIO-True] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-StringIO-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-StringIO-False] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-StringIO-False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-StringIO-None] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-StringIO-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_dont_fuse_from_pandas_ops 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dont_fuse_from_pandas_ops 
dask/dataframe/dask_expr/tests/test_collection.py::test_column_projection_modify_list 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-columns] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_column_projection_modify_list 
dask/dataframe/dask_expr/tests/test_collection.py::test_setitem 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_setitem 
dask/dataframe/dask_expr/tests/test_collection.py::test_set_index_blockwise_culling 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_set_index_blockwise_culling 
dask/dataframe/dask_expr/tests/test_collection.py::test_set_index_with_divisions_already_known 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-columns] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_set_index_with_divisions_already_known 
dask/dataframe/dask_expr/tests/test_collection.py::test_explode 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_explode 
dask/dataframe/dask_expr/tests/test_collection.py::test_explode_simplify 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_explode_simplify 
dask/dataframe/dask_expr/tests/test_collection.py::test_meta_divisions_name 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_meta_divisions_name 
dask/dataframe/dask_expr/tests/test_collection.py::test_meta_blockwise 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_meta_blockwise 
dask/dataframe/dask_expr/tests/test_collection.py::test_dask 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dask 
dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cumsum] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cumsum] 
dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cumprod] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cumprod] 
dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cummin] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cummin] 
dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cummax] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cummax] 
dask/dataframe/dask_expr/tests/test_collection.py::test_bool 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_bool 
dask/dataframe/dask_expr/tests/test_collection.py::test_map_index 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-index] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_map_index 
dask/dataframe/dask_expr/tests/test_collection.py::test_squeeze 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_squeeze 
dask/dataframe/dask_expr/tests/test_collection.py::test_filter_pushdown_reducer_in_predicate 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_filter_pushdown_reducer_in_predicate 
dask/dataframe/dask_expr/tests/test_collection.py::test_assign_empty_columns 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_empty_columns 
dask/dataframe/dask_expr/tests/test_collection.py::test_index_divisions 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_index_divisions 
dask/dataframe/dask_expr/tests/test_collection.py::test_nbytes 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nbytes 
dask/dataframe/dask_expr/tests/test_collection.py::test_pop 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_pop 
dask/dataframe/dask_expr/tests/test_collection.py::test_dot 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-index] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-0] 
[gw1] [ 39%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_dot 
dask/dataframe/dask_expr/tests/test_collection.py::test_pipe 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-columns] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_pipe 
dask/dataframe/dask_expr/tests/test_collection.py::test_mode 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_mode 
dask/dataframe/dask_expr/tests/test_collection.py::test_value_counts 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_value_counts 
dask/dataframe/dask_expr/tests/test_collection.py::test_dropna 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dropna 
dask/dataframe/dask_expr/tests/test_collection.py::test_value_counts_with_dropna 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-index] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_value_counts_with_dropna 
dask/dataframe/dask_expr/tests/test_collection.py::test_fillna 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_fillna 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-ffill-None] 
[gw1] [ 39%] XFAIL dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-ffill-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-ffill-1] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-ffill-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-ffill-2] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-ffill-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-bfill-None] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-0] 
[gw1] [ 39%] XFAIL dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-bfill-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-bfill-1] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-bfill-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-bfill-2] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[index-bfill-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-ffill-None] 
[gw1] [ 39%] XFAIL dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-ffill-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-ffill-1] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-ffill-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-ffill-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-columns] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-ffill-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-bfill-None] 
[gw1] [ 39%] XFAIL dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-bfill-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-bfill-1] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-bfill-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-bfill-2] 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_and_bfill[0-bfill-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index_projections 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index_projections 
dask/dataframe/dask_expr/tests/test_collection.py::test_series_map_meta 
[gw1] [ 39%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_series_map_meta 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-None-1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-None-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-None-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-1h-1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-1h-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-1h-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-1h-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-freq2-1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-freq2-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-freq2-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-freq2-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-None-1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-None-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-None-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-1h-1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-1h-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-1h-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-1h-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-freq2-1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-freq2-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-freq2-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-freq2-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-None-1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-None-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-None-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-1h-1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-1h-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-1h-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-1h-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-freq2-1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-freq2-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-freq2-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-freq2-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-None-1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-None-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-None-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-1h-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-1h-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-1h-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-1h-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-freq2-1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-freq2-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-freq2-2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-freq2-2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_memory_usage 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_memory_usage 
dask/dataframe/dask_expr/tests/test_collection.py::test_nlargest_nsmallest[func0] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nlargest_nsmallest[func0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_nlargest_nsmallest[func1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nlargest_nsmallest[func1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>0] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>1] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>2] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>3] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>3] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>4] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>4] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>5] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>5] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>6] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>6] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>7] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>7] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>8] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>8] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>9] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>9] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>10] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>10] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>11] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>11] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>12] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>12] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>13] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>13] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>14] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>14] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>15] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>15] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>16] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-0] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>16] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>17] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-columns] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>17] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>18] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-1] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>18] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>19] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-index] 
[gw1] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>19] 
dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>20] 
[gw0] [ 39%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>20] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-None] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-None] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-None] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>0] 
[gw1] [ 40%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>1] 
[gw1] [ 40%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>2] 
[gw1] [ 40%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>3] 
[gw1] [ 40%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>3] 
dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>4] 
[gw1] [ 40%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>4] 
dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>5] 
[gw1] [ 40%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_boolean_operators[<lambda>5] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-0] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-0] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-columns] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-columns] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-1] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-1] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-index] 
[gw0] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-index] 
[gw1] [ 40%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-1] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-index] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-0] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-columns] 
[gw0] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-columns] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-spec2] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-1] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-spec2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-spec3] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-index] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-spec3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-spec4] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-spec4] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-sum] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-0] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-sum] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_reduction_shuffle 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_reduction_shuffle 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_projection_split_out 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-columns] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-1] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-index] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_projection_split_out 
dask/dataframe/dask_expr/tests/test_groupby.py::test_numeric_column_names 
[gw0] [ 41%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_numeric_column_names 
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_divisions 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-0] 
[gw0] [ 41%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_divisions 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_co_aligned_grouper 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_co_aligned_grouper 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[True-True-var] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[True-True-var] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[True-True-std] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-columns] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[True-True-std] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[True-False-var] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[True-False-var] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[True-False-std] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[True-False-std] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[False-True-var] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[False-True-var] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[False-True-std] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[False-True-std] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[False-False-var] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-index] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[False-False-var] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[False-False-std] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_var_dropna_observed[False-False-std] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_median 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-columns] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_median 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_apply_args 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-1] 
[gw0] [ 41%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_apply_args 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_ffill_bfill 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-0] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_ffill_bfill 
dask/dataframe/dask_expr/tests/test_groupby.py::test_get_group[<lambda>0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-columns] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_get_group[<lambda>0] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_get_group[<lambda>1] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_get_group[<lambda>1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_get_group[<lambda>2] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-1] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_get_group[<lambda>2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_rolling 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-index] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-0] 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-columns] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_rolling 
dask/dataframe/dask_expr/tests/test_groupby.py::test_rolling_groupby_projection 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-1] 
[gw0] [ 41%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_rolling_groupby_projection 
dask/dataframe/dask_expr/tests/test_groupby.py::test_std_var_slice 
[gw1] [ 41%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-index] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_std_var_slice 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_error 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_error 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_dir 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_dir 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_udf_user_warning 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_udf_user_warning 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_index_array 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-columns] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_index_array 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_median_numeric_only 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_median_numeric_only 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_median_series 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-0] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_median_series 
dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[prod-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-columns] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[prod-0] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[prod-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[prod-1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[prod-2] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-index] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[prod-2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[prod-3] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-0] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[prod-3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[sum-0] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[sum-0] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[sum-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-columns] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[sum-1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[sum-2] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[sum-2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[sum-3] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_with_min_count[sum-3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-a] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-0] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-a] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-c] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-index] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-c] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-d] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-columns] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-d] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-sel3] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-0] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-sel3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-sel4] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-sel4] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-a] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-columns] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-a] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-c] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-0] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-c] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-d] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-d] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-sel3] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-columns] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-sel3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-sel4] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-index] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-sel4] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-a] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-a] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-c] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-0] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-c] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-d] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-d] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-sel3] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-0] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-sel3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-sel4] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-sel4] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-a] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-columns] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-a] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-c] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-index] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-c] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-d] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-d] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-sel3] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-columns] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-sel3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-sel4] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-0] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-sel4] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-a] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-a] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-c] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-columns] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-c] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-d] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-index] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-d] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-sel3] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-sel3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-sel4] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-index] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-sel4] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-a] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-a] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-c] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-columns] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-c] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-d] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-0] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-d] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-sel3] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-index] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-sel3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-sel4] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-sel4] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[3-key1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[3-key1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[3-by1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-index] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[3-by1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[value-key1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[value-key1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[value-by1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[value-by1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key2-key1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-0] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key2-key1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key2-by1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key2-by1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key3-key1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-columns] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key3-key1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key3-by1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key3-by1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key4-key1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key4-key1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key4-by1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key4-by1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key5-key1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key5-key1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key5-by1] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_getitem[slice_key5-by1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumsum-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-index] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumsum-1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumsum-2] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumsum-2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumprod-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-columns] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumprod-1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumprod-2] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-index] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumprod-2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumcount-1] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-columns] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-1] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumcount-1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumcount-2] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-index] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-0] 
[gw1] [ 42%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-columns] 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumcount-2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_not_supported[std] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_not_supported[std] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_not_supported[var] 
[gw0] [ 42%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_not_supported[var] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum 
[gw0] [ 42%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_size_drop_columns 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-1] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-1] 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_size_drop_columns 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_respect_shuffle_context 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-index] 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_respect_shuffle_context 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_meta_error 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_meta_error 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_aggregate_series_split_out 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-0] 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_aggregate_series_split_out 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_rename_columns 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-columns] 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_rename_columns 
dask/dataframe/dask_expr/tests/test_groupby.py::test_get_group_multiple_keys 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_get_group_multiple_keys 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_index_modified_divisions 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_index_modified_divisions 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_getitem_apply_group_keys 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-1] 
[gw0] [ 43%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_getitem_apply_group_keys 
dask/dataframe/dask_expr/tests/test_indexing.py::test_iloc 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_iloc 
dask/dataframe/dask_expr/tests/test_indexing.py::test_iloc_errors 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_iloc_errors 
dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_slice 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_slice 
dask/dataframe/dask_expr/tests/test_indexing.py::test_iloc_slice 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_iloc_slice 
dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[False-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[False-False] 
dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[False-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[False-True] 
dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[True-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[True-False] 
dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[True-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[True-True] 
dask/dataframe/dask_expr/tests/test_indexing.py::test_loc 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_loc 
dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_non_informative_index 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_non_informative_index 
dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_with_series 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_with_series 
dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_with_array 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_with_array 
dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_with_function 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_with_function 
dask/dataframe/dask_expr/tests/test_indexing.py::test_getitem_align 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_indexing.py::test_getitem_align 
dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_bool_cindex 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_bool_cindex 
dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_slicing 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-1] 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_slicing 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-index] 
dask/dataframe/dask_expr/tests/test_indexing.py::test_reverse_indexing 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_reverse_indexing 
dask/dataframe/dask_expr/tests/test_indexing.py::test_indexing_element_index 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_indexing.py::test_indexing_element_index 
dask/dataframe/dask_expr/tests/test_interchange.py::test_interchange_protocol 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_interchange.py::test_interchange_protocol 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_broadcast 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_broadcast 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_merge[True] 
[gw0] [ 43%] FAILED dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_merge[True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_merge[False] 
[gw0] [ 43%] FAILED dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_merge[False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap[1] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap[1] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap[4] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap[4] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_raises 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-1] 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_raises 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_divisions 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_divisions 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_partition_info 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_partition_info 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_provide_meta 
[gw0] [ 43%] FAILED dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_provide_meta 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_errors 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_errors 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_align_dataframes[True] 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_align_dataframes[True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_align_dataframes[False] 
[gw0] [ 43%] PASSED dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_align_dataframes[False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-True] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-True] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-True] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-True] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-True] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-index] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-True] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-True] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-index] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-True] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-True] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-0] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-False] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-True] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-columns] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-1] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-True] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-False] 
[gw1] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-index] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-True] 
[gw0] [ 43%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-0] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-columns] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-False] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-1] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-False] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-index] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-0] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-columns] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-1] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-index] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-0] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-columns] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-1] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-False] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-index] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-False] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-0] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-columns] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-1] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-index] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-0] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-False] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-False] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-columns] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-1] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-index] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-0] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-columns] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-1] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-False] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-index] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-False] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-0] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-columns] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-1] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-index] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-False] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-0] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-columns] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-1] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-index] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-0] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-columns] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-1] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-True] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-False] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-index] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-True] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-0] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-True] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-False] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-False] 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_propagates_index_metadata 
[gw0] [ 44%] FAILED dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_propagates_index_metadata 
dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_token_given 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-columns] 
[gw0] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_token_given 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge[tasks-left] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-1] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge[tasks-left] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-index] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge[tasks-right] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-0] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge[tasks-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge[tasks-inner] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-columns] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge[tasks-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge[tasks-outer] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-1] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-index] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge[tasks-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge[disk-left] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-0] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-columns] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge[disk-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge[disk-right] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-1] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge[disk-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge[disk-inner] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-index] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-0] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge[disk-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge[disk-outer] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-columns] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge[disk-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-True-left] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-True-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-True-right] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-1] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-True-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-True-inner] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-True-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-True-outer] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-index] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-True-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-False-left] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-False-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-False-right] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-0] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-False-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-False-inner] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-False-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-False-outer] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-columns] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-True-False-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-True-left] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-True-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-True-right] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-1] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-True-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-True-inner] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-True-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-True-outer] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-index] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-True-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-False-left] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-False-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-False-right] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-0] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-False-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-False-inner] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-False-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-False-outer] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[tasks-False-False-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-True-left] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-columns] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-True-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-True-right] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-True-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-True-inner] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-1] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-True-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-True-outer] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-True-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-False-left] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-index] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-False-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-False-right] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-False-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-False-inner] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-0] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-False-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-False-outer] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-True-False-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-True-left] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-columns] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-True-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-True-right] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-True-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-True-inner] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-1] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-True-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-True-outer] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-True-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-False-left] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-index] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-False-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-False-right] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-False-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-False-inner] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-0] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-False-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-False-outer] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_indexed[disk-False-False-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[None-left] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-columns] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-1] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[None-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[None-right] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-index] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-0] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[None-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[None-inner] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-columns] 
[gw0] [ 44%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[None-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[None-outer] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-1] 
[gw1] [ 44%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-index] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[None-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[22-left] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-0] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-columns] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[22-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[22-right] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-0] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[22-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[22-inner] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-columns] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[22-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[22-outer] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-1] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_broadcast_merge[22-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_column_projection 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_column_projection 
dask/dataframe/dask_expr/tests/test_merge.py::test_join[tasks-left] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-index] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_join[tasks-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_join[tasks-right] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-0] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-columns] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_join[tasks-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_join[tasks-inner] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-index] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_join[tasks-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_join[tasks-outer] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-0] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-columns] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_join[tasks-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_join[disk-left] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-1] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_join[disk-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_join[disk-right] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-0] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-columns] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_join[disk-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_join[disk-inner] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-1] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_join[disk-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_join[disk-outer] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-0] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_join[disk-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_join_recursive 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-columns] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_join_recursive 
dask/dataframe/dask_expr/tests/test_merge.py::test_join_recursive_raises 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_join_recursive_raises 
dask/dataframe/dask_expr/tests/test_merge.py::test_singleton_divisions 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-index] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_singleton_divisions 
dask/dataframe/dask_expr/tests/test_merge.py::test_categorical_merge_does_not_increase_npartitions 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_categorical_merge_does_not_increase_npartitions 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_len 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-0] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_len 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_optimize_subset_strings 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_optimize_subset_strings 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_combine_similar[2-3] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-1] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_combine_similar[2-3] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_combine_similar[1-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-0] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_combine_similar[1-1] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_combine_similar_intermediate_projections 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-1] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_combine_similar_intermediate_projections 
dask/dataframe/dask_expr/tests/test_merge.py::test_categorical_merge_with_merge_column_cat_in_one_and_not_other_upcasts 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-0] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_categorical_merge_with_merge_column_cat_in_one_and_not_other_upcasts 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_combine_similar_hangs 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-0] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-columns] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_combine_similar_hangs 
dask/dataframe/dask_expr/tests/test_merge.py::test_recursive_join 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-0] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_recursive_join 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_repartition 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-columns] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_repartition 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_reparititon_divisions 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_reparititon_divisions 
dask/dataframe/dask_expr/tests/test_merge.py::test_join_gives_proper_divisions 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_join_gives_proper_divisions 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_known_to_single[inner-tasks] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-1] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_known_to_single[inner-tasks] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_known_to_single[inner-disk] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-index] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_known_to_single[inner-disk] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_known_to_single[left-tasks] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_known_to_single[left-tasks] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_known_to_single[left-disk] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-0] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_known_to_single[left-disk] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_empty_left_df[right] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-columns] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_empty_left_df[right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_empty_left_df[outer] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-index] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_empty_left_df[outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_npartitions 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-0] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_npartitions 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-inner] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-1] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-outer] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-0] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-left] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-1] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-right] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-0] 
[gw0] [ 45%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[False-inner] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[False-inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[False-outer] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[False-outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[False-left] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[False-left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[False-right] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[False-right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_pandas_object 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-columns] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_pandas_object 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-1-left-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-1-left-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-1-left-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-0] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-1-left-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-1-outer-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-1-outer-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-1-outer-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-1] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-1-outer-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-2-left-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-2-left-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-2-left-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-0] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-columns] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-2-left-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-2-outer-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-2-outer-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-2-outer-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-1] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-2-outer-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-3-left-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-3-left-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-3-left-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-0] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-columns] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-3-left-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-3-outer-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-3-outer-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-3-outer-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-0] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[1-3-outer-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-1-left-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-1-left-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-1-left-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-index] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-1-left-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-1-outer-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-1-outer-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-1-outer-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-0] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-columns] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-1-outer-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-2-left-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-2-left-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-2-left-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-0] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-2-left-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-2-outer-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-2-outer-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-2-outer-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-index] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-2-outer-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-3-left-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-3-left-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-3-left-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-0] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-1] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-3-left-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-3-outer-True] 
[gw0] [ 45%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-3-outer-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-3-outer-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-0] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-1] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[2-3-outer-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-1-left-True] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-0] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-0] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-1-left-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-1-left-False] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-0] 
[gw0] [ 45%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-1-left-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-1-outer-True] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-columns] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-1] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-index] 
[gw1] [ 45%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-0] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-columns] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-1] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-index] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-1-outer-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-1-outer-False] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-0] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-columns] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-1] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-index] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-1-outer-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-2-left-True] 
[gw0] [ 46%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-2-left-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-2-left-False] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-0] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-columns] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-1] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-index] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-2-left-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-2-outer-True] 
[gw0] [ 46%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-2-outer-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-2-outer-False] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-0] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-columns] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-2-outer-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-3-left-True] 
[gw0] [ 46%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-3-left-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-3-left-False] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-index] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-0] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-columns] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-3-left-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-3-outer-True] 
[gw0] [ 46%] SKIPPED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-3-outer-True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-3-outer-False] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-index] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-0] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-columns] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_pairwise_merge_results_in_identical_output_df[3-3-outer-False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_filter_merge 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-index] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-0] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-columns] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-1] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-index] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_filter_merge 
dask/dataframe/dask_expr/tests/test_merge.py::test_filter_merge_suffixes 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-0] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-columns] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_filter_merge_suffixes 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_filter_renaming_columns 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_filter_renaming_columns 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_avoid_overeager_filter_pushdown 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-index] 
[gw0] [ 46%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_avoid_overeager_filter_pushdown 
dask/dataframe/dask_expr/tests/test_merge.py::test_join_consistent_index_names 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-0] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_join_consistent_index_names 
dask/dataframe/dask_expr/tests/test_merge.py::test_isin_filter_pushdown[left] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-columns] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_isin_filter_pushdown[left] 
dask/dataframe/dask_expr/tests/test_merge.py::test_isin_filter_pushdown[inner] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-1] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-index] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_isin_filter_pushdown[inner] 
dask/dataframe/dask_expr/tests/test_merge.py::test_isin_filter_pushdown[right] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-0] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_isin_filter_pushdown[right] 
dask/dataframe/dask_expr/tests/test_merge.py::test_isin_filter_pushdown[outer] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-columns] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_isin_filter_pushdown[outer] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_filter_pushdown_broadcast 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_filter_pushdown_broadcast 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_filter_stuck_between_merges 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_filter_stuck_between_merges 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_or_two_branches 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_or_two_branches 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_or_two_branches_no_pushdown 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_or_two_branches_no_pushdown 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_filter_column_used_multiple_times 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-index] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_filter_column_used_multiple_times 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_scalar_comparison 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_scalar_comparison 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_leftsemi 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-0] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_leftsemi 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_suffix_projections 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_suffix_projections 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_after_rename[True] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-columns] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_after_rename[True] 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_after_rename[False] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_after_rename[False] 
dask/dataframe/dask_expr/tests/test_merge.py::test_mismatching_meta 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-1] 
[gw0] [ 46%] FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_mismatching_meta 
dask/dataframe/dask_expr/tests/test_merge.py::test_merge_tuple_left_on 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-index] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_tuple_left_on 
dask/dataframe/dask_expr/tests/test_merge.py::test_merged_partitions_filtered 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge.py::test_merged_partitions_filtered 
dask/dataframe/dask_expr/tests/test_merge_asof.py::test_merge_asof_indexed 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge_asof.py::test_merge_asof_indexed 
dask/dataframe/dask_expr/tests/test_merge_asof.py::test_merge_asof_indexed_optimiser 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-0] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge_asof.py::test_merge_asof_indexed_optimiser 
dask/dataframe/dask_expr/tests/test_merge_asof.py::test_merge_asof_on_basic 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge_asof.py::test_merge_asof_on_basic 
dask/dataframe/dask_expr/tests/test_merge_asof.py::test_merge_asof_one_partition 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_merge_asof.py::test_merge_asof_one_partition 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_groupby_implicit_divisions 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-columns] 
[gw0] [ 46%] XFAIL dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_groupby_implicit_divisions 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_groupby_avoid_shuffle 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_groupby_avoid_shuffle 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>0] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>0] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>1] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>2] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-index] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>2] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>3] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>3] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>4] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-0] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>4] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>5] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>5] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>6] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-columns] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>6] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>7] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_when_necessary[<lambda>7] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>0] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>0] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>1] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>2] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>2] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>3] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-index] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>3] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>4] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>4] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>5] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-0] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>5] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>6] 
[gw0] [ 46%] FAILED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>6] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>7] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-columns] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-1] 
[gw0] [ 46%] FAILED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>7] 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_repartition 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_repartition 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_merge_avoid_shuffle 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-index] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_merge_avoid_shuffle 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_merge_shuffle_if_different_order 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_merge_shuffle_if_different_order 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_after_only_blockwise_merge 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_shuffle_after_only_blockwise_merge 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_on_top_of_lowered_shuffle 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_on_top_of_lowered_shuffle 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_merge_groupby_to_frame 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-0] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-columns] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_merge_groupby_to_frame 
dask/dataframe/dask_expr/tests/test_predicate_pushdown.py::test_rewrite_filters 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-1] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_predicate_pushdown.py::test_rewrite_filters 
dask/dataframe/dask_expr/tests/test_predicate_pushdown.py::test_rewrite_filters_query 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_predicate_pushdown.py::test_rewrite_filters_query 
dask/dataframe/dask_expr/tests/test_quantiles.py::test_repartition_quantiles 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-index] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_quantiles.py::test_repartition_quantiles 
dask/dataframe/dask_expr/tests/test_reductions.py::test_median 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-0] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_median 
dask/dataframe/dask_expr/tests/test_reductions.py::test_min_dt 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-columns] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_min_dt 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-False-series0] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-False-series0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-False-series1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-False-series1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-False-series2] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-False-series2] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-False-series3] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-False-series3] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-False-series4] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-False-series4] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-True-series0] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-True-series0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-True-series1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-True-series1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-True-series2] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-True-series2] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-True-series3] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-True-series3] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-True-series4] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Series-True-series4] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-False-series0] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-False-series0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-False-series1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-False-series1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-False-series2] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-False-series2] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-False-series3] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-False-series3] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-False-series4] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-False-series4] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-True-series0] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-True-series0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-True-series1] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-True-series1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-True-series2] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-True-series2] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-True-series3] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-True-series3] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-True-series4] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_monotonic[Index-True-series4] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[1-False] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[1-False] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[1-None] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-index] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[1-None] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[1-5] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[1-5] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[True-False] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-0] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[True-False] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[True-None] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-columns] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[True-None] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[True-5] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[True-5] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_reduction_name 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-1] 
[gw0] [ 46%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_series_reduction_name 
dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[1-False] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[1-False] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[1-None] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-index] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[1-None] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[1-5] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-0] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[1-5] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[True-False] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[True-False] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[True-None] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-columns] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[True-None] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[True-5] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[True-5] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts_sort 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-1] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts_sort 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[1-None] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-index] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[1-None] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[1-5] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[1-5] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[True-None] 
[gw1] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-0] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[True-None] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[True-5] 
[gw0] [ 46%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[True-5] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-sum] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-sum] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-prod] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-prod] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-min] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-min] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-max] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-max] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-any] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-any] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-all] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-all] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-count] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-count] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-sum] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-sum] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-prod] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-prod] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-min] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-min] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-max] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-max] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-any] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-any] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-all] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-all] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-count] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-count] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-sum] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-sum] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-prod] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-prod] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-min] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-min] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-max] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-index] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-max] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-any] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-any] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-all] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-all] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-count] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-count] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-sum] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-sum] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-prod] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-prod] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-min] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-min] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-max] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-max] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-any] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-any] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-all] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-all] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-count] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-count] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[False-53] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[False-53] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[None-57] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[None-57] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[5-57] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[5-57] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[2-73] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[2-73] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-sum] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-sum] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-prod] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-prod] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-min] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-min] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-max] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-max] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-any] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-any] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-all] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-all] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-mode] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-mode] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-count] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-count] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-sum] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-sum] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-prod] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-prod] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-min] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-min] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-max] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-max] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-any] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-any] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-all] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-all] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-mode] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-mode] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-count] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-count] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-sum] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-sum] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-prod] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-prod] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-min] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-min] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-max] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-max] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-any] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-any] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-all] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-all] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-mode] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-0] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-mode] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-count] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-count] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-sum] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-sum] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-prod] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-prod] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-min] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-min] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-max] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-max] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-any] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-any] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-all] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-all] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-mode] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-mode] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-count] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-count] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_with_strings 
[gw0] [ 47%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_with_strings 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[sum--1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[sum--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[sum-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[sum-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[sum-1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[sum-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[prod--1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[prod--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[prod-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[prod-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[prod-1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[prod-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[min--1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[min--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[min-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[min-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[min-1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[min-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[max--1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[max--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[max-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[max-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[max-1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[max-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[any--1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[any--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[any-0] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[any-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[any-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[any-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[all--1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[all--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[all-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[all-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[all-1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[all-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[mode--1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[mode--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[mode-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[mode-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[mode-1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[mode-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[count--1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[count--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[count-0] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[count-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[count-1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[count-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[nunique_approx--1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[nunique_approx--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[nunique_approx-0] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-0] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[nunique_approx-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[nunique_approx-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[nunique_approx-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[drop_duplicates--1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[drop_duplicates--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[drop_duplicates-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[drop_duplicates-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[drop_duplicates-1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[drop_duplicates-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[unique--1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[unique--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[unique-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[unique-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[unique-1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[unique-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[value_counts--1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[value_counts--1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[value_counts-0] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-1] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[value_counts-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[value_counts-1] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-index] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[value_counts-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[None] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[None] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[False] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-0] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[False] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[2] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[2] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[10] 
[gw1] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-columns] 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[10] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unique_base 
[gw0] [ 47%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unique_base 
dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts_split_out_normalize 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-1] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts_split_out_normalize 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[0-sum] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[0-sum] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[0-prod] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[0-prod] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[0-product] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[0-product] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[9-sum] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[9-sum] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[9-prod] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[9-prod] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[9-product] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_series_agg_with_min_count[9-product] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func0] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-index] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func1] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func2] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-0] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func2] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func3] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-columns] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func3] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func4] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func4] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func5] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-1] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func5] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func6] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-index] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func6] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func7] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func7] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func8] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-0] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func8] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func9] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func9] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func10] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-columns] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func10] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func11] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-1] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func11] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func12] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func12] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func13] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-index] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func13] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[<lambda>] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-0] 
[gw0] [ 48%] XFAIL dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[<lambda>] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_skew_kurt 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_skew_kurt 
dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func0] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-columns] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func1] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func2] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-1] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func2] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func3] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-index] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func3] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[<lambda>] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-0] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[<lambda>] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[<lambda>] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-columns] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[<lambda>] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func1] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-1] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func2] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func2] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func3] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-index] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func3] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func4] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func4] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func5] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-0] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func5] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func6] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-columns] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func6] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func7] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func7] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_cov_corr 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-1] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_cov_corr 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_on_empty_df 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_on_empty_df 
dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[1-True-0] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-index] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[1-True-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[1-True-1] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[1-True-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[1-False-0] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[1-False-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[1-False-1] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[1-False-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[2-True-0] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-0] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[2-True-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[2-True-1] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[2-True-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[2-False-0] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[2-False-0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[2-False-1] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_std_kwargs[2-False-1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_mean_series_axis_none 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-columns] 
[gw0] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_mean_series_axis_none 
dask/dataframe/dask_expr/tests/test_reductions.py::test_mode_numeric_only 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-1] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_mode_numeric_only 
dask/dataframe/dask_expr/tests/test_reductions.py::test_divmod 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_divmod 
dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts_with_normalize 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-index] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts_with_normalize 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_method 
[gw0] [ 48%] FAILED dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_method 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_method_split_every 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-0] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_method_split_every 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_split_every_false 
[gw0] [ 48%] FAILED dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_split_every_false 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unique_numerical_columns[0] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_unique_numerical_columns[0] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unique_numerical_columns[1] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_unique_numerical_columns[1] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_unique_numerical_columns[2] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-columns] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_unique_numerical_columns[2] 
dask/dataframe/dask_expr/tests/test_reductions.py::test_cat_value_counts_large_unknown_categories 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-1] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-index] 
[gw0] [ 48%] FAILED dask/dataframe/dask_expr/tests/test_reductions.py::test_cat_value_counts_large_unknown_categories 
dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions_timestamps_display 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions_timestamps_display 
dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts_shuffle_properly 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts_shuffle_properly 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs0] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-0] 
[gw0] [ 48%] FAILED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs0] 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs1] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-columns] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-1] 
[gw0] [ 48%] FAILED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs1] 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs2] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-index] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-0] 
[gw0] [ 48%] FAILED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs2] 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs3] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-columns] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-1] 
[gw0] [ 48%] FAILED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs3] 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_noop[<lambda>] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_noop[<lambda>] 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_noop[tuple] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_noop[tuple] 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_noop[list] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_noop[list] 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_freq 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_freq 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_freq_errors 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_freq_errors 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_npartitions_numeric_edge_case 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_npartitions_numeric_edge_case 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_empty_partitions_dtype 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_empty_partitions_dtype 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_filter_pushdown 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_filter_pushdown 
dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_unknown_divisions 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_unknown_divisions 
dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[first] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-index] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-0] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-columns] 
[gw0] [ 48%] FAILED dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[first] 
dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[last] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-1] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-index] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-0] 
[gw0] [ 48%] FAILED dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[last] 
dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[sum] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-columns] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[sum] 
dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[mean] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-1] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-index] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[mean] 
dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[count] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-0] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-columns] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[count] 
dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table_fails 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-1] 
[gw0] [ 48%] FAILED dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table_fails 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-count-how_args0] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-mean-how_args1] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-index] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-sum-how_args2] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-min-how_args3] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-max-how_args4] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-var-how_args5] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-var-how_args6] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-std-how_args7] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-0] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-std-how_args8] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-median-how_args9] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-skew-how_args10] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-quantile-how_args11] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-kurt-how_args12] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-count-how_args0] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-columns] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-mean-how_args1] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-sum-how_args2] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-min-how_args3] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-max-how_args4] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-var-how_args5] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-var-how_args6] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-std-how_args7] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-1] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-std-how_args8] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-median-how_args9] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-skew-how_args10] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-quantile-how_args11] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-kurt-how_args12] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-1-None-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-count-how_args0] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-mean-how_args1] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-index] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-sum-how_args2] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-min-how_args3] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-max-how_args4] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-var-how_args5] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-var-how_args6] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-std-how_args7] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-std-how_args8] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-0] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-median-how_args9] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-skew-how_args10] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-quantile-how_args11] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-kurt-how_args12] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-count-how_args0] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-mean-how_args1] 
[gw1] [ 48%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-columns] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-sum-how_args2] 
[gw0] [ 48%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-min-how_args3] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-max-how_args4] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-var-how_args6] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-std-how_args7] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-std-how_args8] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-median-how_args9] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-skew-how_args10] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-quantile-how_args11] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-kurt-how_args12] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-2-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-count-how_args0] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-index] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-mean-how_args1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-sum-how_args2] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-min-how_args3] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-max-how_args4] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-var-how_args6] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-std-how_args7] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-std-how_args8] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-columns] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-median-how_args9] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-skew-how_args10] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-quantile-how_args11] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-kurt-how_args12] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-count-how_args0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-mean-how_args1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-sum-how_args2] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-min-how_args3] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-max-how_args4] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-var-how_args6] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-std-how_args7] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-std-how_args8] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-median-how_args9] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-skew-how_args10] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-quantile-how_args11] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-kurt-how_args12] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-True-3-3-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-count-how_args0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-mean-how_args1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-sum-how_args2] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-index] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-min-how_args3] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-max-how_args4] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-var-how_args6] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-std-how_args7] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-std-how_args8] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-std-how_args8] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-median-how_args9] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-skew-how_args10] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-quantile-how_args11] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-kurt-how_args12] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-count-how_args0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-mean-how_args1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-sum-how_args2] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-columns] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-min-how_args3] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-max-how_args4] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-var-how_args6] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-std-how_args7] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-std-how_args8] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-median-how_args9] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-skew-how_args10] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-quantile-how_args11] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-kurt-how_args12] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-1-None-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-count-how_args0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-mean-how_args1] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-index] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-sum-how_args2] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-min-how_args3] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-max-how_args4] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-var-how_args6] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-std-how_args7] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-std-how_args8] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-median-how_args9] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-skew-how_args10] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-quantile-how_args11] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-kurt-how_args12] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-count-how_args0] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-columns] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-mean-how_args1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-sum-how_args2] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-min-how_args3] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-max-how_args4] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-var-how_args6] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-std-how_args7] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-std-how_args8] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-median-how_args9] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-skew-how_args10] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-quantile-how_args11] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-index] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-kurt-how_args12] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-2-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-count-how_args0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-mean-how_args1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-sum-how_args2] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-min-how_args3] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-max-how_args4] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-var-how_args6] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-std-how_args7] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-std-how_args8] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-median-how_args9] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-skew-how_args10] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-columns] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-quantile-how_args11] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-kurt-how_args12] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-count-how_args0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-mean-how_args1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-sum-how_args2] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-min-how_args3] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-max-how_args4] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-var-how_args6] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-std-how_args7] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-std-how_args8] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-index] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-median-how_args9] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-skew-how_args10] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-quantile-how_args11] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-kurt-how_args12] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[1-False-3-3-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-count-how_args0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-mean-how_args1] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-sum-how_args2] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-min-how_args3] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-max-how_args4] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-var-how_args6] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-columns] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-std-how_args7] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-std-how_args8] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-median-how_args9] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-skew-how_args10] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-quantile-how_args11] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-kurt-how_args12] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-count-how_args0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-mean-how_args1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-sum-how_args2] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-sum-how_args2] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-index] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-max-how_args4] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-var-how_args6] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-std-how_args7] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-std-how_args8] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-0] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-median-how_args9] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-skew-how_args10] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-quantile-how_args11] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-kurt-how_args12] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-1-None-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-count-how_args0] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-columns] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-mean-how_args1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-sum-how_args2] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-min-how_args3] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-1] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-max-how_args4] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-var-how_args5] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-var-how_args6] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-index] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-std-how_args7] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-std-how_args8] 
[gw0] [ 49%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-median-how_args9] 
[gw1] [ 49%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-skew-how_args10] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-quantile-how_args11] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-kurt-how_args12] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-True-kurt-how_args12] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-columns] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-mean-how_args1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-sum-how_args2] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-min-how_args3] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-max-how_args4] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-var-how_args5] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-var-how_args6] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-index] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-std-how_args7] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-std-how_args8] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-median-how_args9] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-skew-how_args10] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-quantile-how_args11] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-kurt-how_args12] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-2-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-count-how_args0] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-columns] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-mean-how_args1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-sum-how_args2] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-min-how_args3] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-max-how_args4] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-var-how_args5] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-var-how_args6] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-index] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-std-how_args7] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-std-how_args8] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-median-how_args9] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-skew-how_args10] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-quantile-how_args11] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-kurt-how_args12] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-count-how_args0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-mean-how_args1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-sum-how_args2] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-min-how_args3] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-columns] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-max-how_args4] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-var-how_args5] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-var-how_args6] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-std-how_args7] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-std-how_args8] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-median-how_args9] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-index] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-skew-how_args10] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-quantile-how_args11] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-kurt-how_args12] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-True-3-3-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-count-how_args0] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-mean-how_args1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-sum-how_args2] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-min-how_args3] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-max-how_args4] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-var-how_args5] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-columns] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-var-how_args6] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-std-how_args7] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-std-how_args8] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-median-how_args9] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-skew-how_args10] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-quantile-how_args11] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-kurt-how_args12] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-count-how_args0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-mean-how_args1] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-index] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-sum-how_args2] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-min-how_args3] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-max-how_args4] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-var-how_args5] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-var-how_args6] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-std-how_args7] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-std-how_args8] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-median-how_args9] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-skew-how_args10] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-quantile-how_args11] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-kurt-how_args12] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-columns] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-1-None-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-count-how_args0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-mean-how_args1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-sum-how_args2] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-min-how_args3] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-max-how_args4] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-var-how_args5] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-index] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-var-how_args6] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-std-how_args7] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-std-how_args8] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-median-how_args9] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-skew-how_args10] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-quantile-how_args11] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-quantile-how_args11] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-columns] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-count-how_args0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-mean-how_args1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-sum-how_args2] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-min-how_args3] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-max-how_args4] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-var-how_args5] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-index] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-var-how_args6] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-std-how_args7] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-std-how_args8] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-std-how_args8] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-median-how_args9] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-skew-how_args10] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-quantile-how_args11] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-kurt-how_args12] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-columns] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-2-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-count-how_args0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-mean-how_args1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-sum-how_args2] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-min-how_args3] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-max-how_args4] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-var-how_args5] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-index] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-var-how_args6] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-std-how_args7] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-std-how_args8] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-median-how_args9] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-skew-how_args10] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-quantile-how_args11] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-kurt-how_args12] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-columns] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-True-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-count-how_args0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-count-how_args0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-mean-how_args1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-mean-how_args1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-sum-how_args2] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-sum-how_args2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-min-how_args3] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-1] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-min-how_args3] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-max-how_args4] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-max-how_args4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-var-how_args5] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-var-how_args5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-var-how_args6] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-index] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-var-how_args6] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-std-how_args7] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-std-how_args7] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-std-how_args8] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-std-how_args8] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-median-how_args9] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-median-how_args9] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-skew-how_args10] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-0] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-skew-how_args10] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-quantile-how_args11] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-quantile-how_args11] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-kurt-how_args12] 
[gw0] [ 50%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apis[2-False-3-3-False-kurt-how_args12] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[1-1] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-columns] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[1-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[1-2] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[1-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[2-1] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[2-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[2-2] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[2-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-1-1] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-1-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-1-2] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-1-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-2-1] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-1] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-2-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-2-2] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-2-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-1-1] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-1-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-1-2] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-1-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-2-1] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-2-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-2-2] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-index] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-2-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-1-1] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-1-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-1-2] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-1-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-2-1] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-2-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-2-2] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-2-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-1-1] 
[gw1] [ 50%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-index] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-1-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-1-2] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-1-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-2-1] 
[gw0] [ 50%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-2-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-2-2] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-2-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-1-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-1-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-1-2] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-1-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-2-1] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-columns] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-2-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-2-2] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-2-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-1-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-1-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-1-2] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-1-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-2-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-2-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-2-2] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-2-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-1-1] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-1-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-1-2] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-1-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-2-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-2-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-2-2] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-2-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-1-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-1-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-1-2] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-1-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-2-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-2-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-2-2] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-index] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-2-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_one_element_window 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_one_element_window 
dask/dataframe/dask_expr/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[2s] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-0] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-columns] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[2s] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[5s] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-1] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[5s] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[20s] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-index] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-0] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[20s] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[10h] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-columns] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-1] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[10h] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_one_element_window_empty_after 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_one_element_window_empty_after 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[True-1] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[True-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[True-2] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-index] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[True-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[True-4] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[True-4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[True-5] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-0] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[True-5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[False-1] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[False-1] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[False-2] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-columns] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[False-2] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[False-4] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[False-4] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[False-5] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-1] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_cov[False-5] 
dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_raises 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_raises 
dask/dataframe/dask_expr/tests/test_rolling.py::test_time_rolling_constructor 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_rolling.py::test_time_rolling_constructor 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_disk_shuffle[3-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-index] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_disk_shuffle[3-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_disk_shuffle[3-False] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-0] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_disk_shuffle[3-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_disk_shuffle[6-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-columns] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_disk_shuffle[6-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_disk_shuffle[6-False] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-1] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_disk_shuffle[6-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[32-8-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-index] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[32-8-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[32-8-False] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-0] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-columns] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[32-8-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[32-12-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-1] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[32-12-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[32-12-False] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-index] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[32-12-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[6-8-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-0] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-columns] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[6-8-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[6-8-False] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-1] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[6-8-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[6-12-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-index] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-0] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[6-12-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[6-12-False] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-columns] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle[6-12-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[32-3] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-1] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-index] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[32-3] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[32-12] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-0] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[32-12] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[8-3] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-columns] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[8-3] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[8-12] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-index] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[8-12] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_str_column_not_in_dataframe 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_str_column_not_in_dataframe 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_mixed_list_column_not_in_dataframe 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_mixed_list_column_not_in_dataframe 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_list_column_not_in_dataframe 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_list_column_not_in_dataframe 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_column_columns 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_column_columns 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_column_projection 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_column_projection 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_reductions 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_reductions 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_reductions_after_projection 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-0] 
[gw0] [ 51%] XFAIL dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_reductions_after_projection 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-a-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-columns] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-1] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-index] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-a-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-a-False] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-a-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-b-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-0] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-b-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-b-False] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-columns] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-1] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-index] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-0] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-b-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-by2-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-columns] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-1] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-index] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-0] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-by2-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-by2-False] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-by2-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-a-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-columns] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-1] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-index] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-0] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-a-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-a-False] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-a-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-b-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-columns] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-b-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-b-False] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-1] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-index] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-0] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-columns] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-b-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-by2-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-1] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-index] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-0] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-by2-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-by2-False] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-by2-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-True] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-False] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-columns] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-ascending2] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-ascending2] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-ascending3] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-ascending3] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-True] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-False] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-ascending2] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-ascending2] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-ascending3] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-ascending3] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-True] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-False] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-ascending2] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-ascending2] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-ascending3] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-ascending3] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-True] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-False] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-ascending2] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-ascending2] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-ascending3] 
[gw0] [ 51%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-ascending3] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[1.0-128000000.0] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-1] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-index] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[1.0-128000000.0] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[1.0-12800000.0] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-0] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-columns] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[1.0-12800000.0] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[2.0-128000000.0] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-1] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-index] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-0] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[2.0-128000000.0] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[2.0-12800000.0] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-columns] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[2.0-12800000.0] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_sorted 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-index] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_sorted 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_pre_sorted 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-0] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_pre_sorted 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[True-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-columns] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[True-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[True-False] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-index] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-0] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-columns] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[True-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[False-True] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-index] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-0] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[False-True] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[False-False] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-columns] 
[gw1] [ 51%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-1] 
[gw0] [ 51%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[False-False] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_repartition 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-index] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-0] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-columns] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_repartition 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_simplify 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-1] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-index] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_simplify 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_numeric_columns 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-0] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-columns] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_numeric_columns 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_without_sort 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-1] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_without_sort 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values[None] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-index] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-0] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values[None] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values[tasks] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-columns] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-1] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-index] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-0] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values[tasks] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_temporary_column_dropped[None] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_temporary_column_dropped[None] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_temporary_column_dropped[tasks] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_temporary_column_dropped[tasks] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_optimize 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-columns] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-1] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-index] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_optimize 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_single_partition 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_single_partition 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_list 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-0] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-columns] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_list 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_descending 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-1] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-index] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-0] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-columns] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-1] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_descending 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_head_nlargest 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_head_nlargest 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_tail_nsmallest 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_tail_nsmallest 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_conflicting_ascending_head_tail[1-[True, False]] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_conflicting_ascending_head_tail[1-[True, False]] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-1] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_conflicting_ascending_head_tail[1-[False, True]] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-index] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_conflicting_ascending_head_tail[1-[False, True]] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_conflicting_ascending_head_tail[3-[True, False]] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_conflicting_ascending_head_tail[3-[True, False]] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_conflicting_ascending_head_tail[3-[False, True]] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_conflicting_ascending_head_tail[3-[False, True]] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_head_nlargest_string 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-0] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-columns] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-1] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-index] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_head_nlargest_string 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_head_nlargest 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-0] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-columns] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-1] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_head_nlargest 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_head_nlargest_string 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-index] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-0] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-columns] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_head_nlargest_string 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_filter_sort 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-1] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-index] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_filter_sort 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_add 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-0] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-columns] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_add 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_index_nulls[None] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_index_nulls[None] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_index_nulls[null_value1] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_index_nulls[null_value1] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_index_nulls[null_value2] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_index_nulls[null_value2] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_with_dask_dt_index[16h] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_with_dask_dt_index[16h] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_with_dask_dt_index[-16h] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-1] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-index] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_with_dask_dt_index[-16h] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_sort_values_shuffle_options 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-0] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-columns] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_sort_values_shuffle_options 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_predicate_pushdown 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-1] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-index] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_predicate_pushdown 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_with_explicit_divisions 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-0] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-columns] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_with_explicit_divisions 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_npartitions_changes 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-1] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-index] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-0] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_npartitions_changes 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_sorted_divisions 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_sorted_divisions 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_sort_values_one_partition 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_sort_values_one_partition 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_triggers_calc_when_accessing_divisions 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_triggers_calc_when_accessing_divisions 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-columns] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-1] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_empty_partitions 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-index] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_empty_partitions 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_no_assign 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_no_assign 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_filter_pushdown[shuffle] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_filter_pushdown[shuffle] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_filter_pushdown[sort_values] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_filter_pushdown[sort_values] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_avoid_overeager_filter_pushdown[set_index] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_avoid_overeager_filter_pushdown[set_index] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_avoid_overeager_filter_pushdown[sort_values] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_avoid_overeager_filter_pushdown[sort_values] 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_filter_pushdown 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_filter_pushdown 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_index_shuffle 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_shuffle.py::test_shuffle_index_shuffle 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_user_divisions_one_partition 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-0] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-columns] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_user_divisions_one_partition 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_divisions_npartitions 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-1] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-index] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_divisions_npartitions 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_unordered_categorical 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-0] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_unordered_categorical 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_before_assign 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-columns] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_before_assign 
dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_shuffle_afterwards 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-1] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-index] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-0] 
[gw0] [ 52%] FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_shuffle_afterwards 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[len-kwargs0] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[len-kwargs0] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[capitalize-kwargs1] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[capitalize-kwargs1] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[casefold-kwargs2] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[casefold-kwargs2] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[contains-kwargs3] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[contains-kwargs3] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[count-kwargs4] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[count-kwargs4] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[endswith-kwargs5] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[endswith-kwargs5] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[extract-kwargs6] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-columns] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[extract-kwargs6] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[extractall-kwargs7] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[extractall-kwargs7] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[find-kwargs8] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[find-kwargs8] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[findall-kwargs9] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[findall-kwargs9] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[fullmatch-kwargs10] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[fullmatch-kwargs10] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[get-kwargs11] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[get-kwargs11] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isalnum-kwargs12] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isalnum-kwargs12] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isalpha-kwargs13] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isalpha-kwargs13] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isdecimal-kwargs14] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isdecimal-kwargs14] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isdigit-kwargs15] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-1] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isdigit-kwargs15] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[islower-kwargs16] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[islower-kwargs16] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isspace-kwargs17] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isspace-kwargs17] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[istitle-kwargs18] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[istitle-kwargs18] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isupper-kwargs19] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[isupper-kwargs19] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[join-kwargs20] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[join-kwargs20] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[len-kwargs21] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[len-kwargs21] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[ljust-kwargs22] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[ljust-kwargs22] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[lower-kwargs23] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[lower-kwargs23] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[lstrip-kwargs24] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[lstrip-kwargs24] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[match-kwargs25] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[match-kwargs25] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[normalize-kwargs26] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-index] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[normalize-kwargs26] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[pad-kwargs27] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[pad-kwargs27] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[removeprefix-kwargs28] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[removeprefix-kwargs28] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[removesuffix-kwargs29] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[removesuffix-kwargs29] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[repeat-kwargs30] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[repeat-kwargs30] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[replace-kwargs31] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[replace-kwargs31] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[rfind-kwargs32] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[rfind-kwargs32] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[rjust-kwargs33] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[rjust-kwargs33] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[rstrip-kwargs34] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[rstrip-kwargs34] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[slice-kwargs35] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-0] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[slice-kwargs35] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[slice_replace-kwargs36] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[slice_replace-kwargs36] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[startswith-kwargs37] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[startswith-kwargs37] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[strip-kwargs38] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[strip-kwargs38] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[swapcase-kwargs39] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[swapcase-kwargs39] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[title-kwargs40] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[title-kwargs40] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[upper-kwargs41] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[upper-kwargs41] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[wrap-kwargs42] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[wrap-kwargs42] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[zfill-kwargs43] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[zfill-kwargs43] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[split-kwargs44] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-columns] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[split-kwargs44] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[rsplit-kwargs45] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[rsplit-kwargs45] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[cat-kwargs46] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[cat-kwargs46] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[cat-kwargs47] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_string_accessor[cat-kwargs47] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_str_accessor_cat 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_str_accessor_cat 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_str_split_[range_index] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_str_split_[range_index] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_str_split_[other index] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_str_split_[other index] 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_str_accessor_not_available 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-1] 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_str_accessor_not_available 
dask/dataframe/dask_expr/tests/test_string_accessor.py::test_partition 
[gw0] [ 52%] PASSED dask/dataframe/dask_expr/tests/test_string_accessor.py::test_partition 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_ufunc 
[gw0] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_ufunc 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[mean] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-index] 
[gw0] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[mean] 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[std] 
[gw0] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[std] 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[sum] 
[gw1] [ 52%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-0] 
[gw0] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[sum] 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[cumsum] 
[gw0] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[cumsum] 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[cumprod] 
[gw0] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[cumprod] 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[var] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-columns] 
[gw0] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[var] 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[min] 
[gw0] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[min] 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[max] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-1] 
[gw0] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[max] 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[all] 
[gw0] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[all] 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[any] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-index] 
[gw0] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[any] 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[prod] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-0] 
[gw0] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[prod] 
dask/dataframe/dask_expr/tests/test_ufunc.py::test_clip 
[gw0] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_clip 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text[read_csv-files0] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text[read_table-files1] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text[read_fwf-files2] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text[read_fwf-files2] 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_kwargs[read_csv-files0] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_kwargs[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_kwargs[read_table-files1] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_kwargs[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_kwargs[read_fwf-files2] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_kwargs[read_fwf-files2] 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_dtype_coercion[read_csv-files0] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-columns] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_dtype_coercion[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_dtype_coercion[read_table-files1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_dtype_coercion[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_dtype_coercion[read_fwf-files2] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_dtype_coercion[read_fwf-files2] 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_with_header[read_csv-files0] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_with_header[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_with_header[read_table-files1] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_with_header[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_with_header[read_fwf-files2] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_with_header[read_fwf-files2] 
dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_simple[read_csv-files0] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_simple[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_simple[read_table-files1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_simple[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_simple[read_fwf-files2] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-index] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_simple[read_fwf-files2] 
dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_kwargs[read_csv-files0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_kwargs[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_kwargs[read_table-files1] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_kwargs[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_kwargs[read_fwf-files2] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-columns] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_kwargs[read_fwf-files2] 
dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_blocked[read_csv-files0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_blocked[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_blocked[read_table-files1] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_blocked[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_blocked[read_fwf-files2] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_blocked[read_fwf-files2] 
dask/dataframe/io/tests/test_csv.py::test_skiprows[read_csv-read_csv-files0] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-index] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_skiprows[read_csv-read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_skiprows[read_table-read_table-files1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_skiprows[read_table-read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_comment[read_csv-read_csv-files0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_comment[read_csv-read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_comment[read_table-read_table-files1] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_comment[read_table-read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_skipfooter[read_csv-read_csv-files0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_skipfooter[read_csv-read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_skipfooter[read_table-read_table-files1] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-columns] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_skipfooter[read_table-read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_skiprows_as_list[read_csv-read_csv-files0-str, int, int\n] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_skiprows_as_list[read_csv-read_csv-files0-str, int, int\n] 
dask/dataframe/io/tests/test_csv.py::test_skiprows_as_list[read_table-read_table-files1-str\t int\t int\n] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_skiprows_as_list[read_table-read_table-files1-str\t int\t int\n] 
dask/dataframe/io/tests/test_csv.py::test_enforce_dtypes[read_csv-blocks0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_enforce_dtypes[read_csv-blocks0] 
dask/dataframe/io/tests/test_csv.py::test_enforce_dtypes[read_table-blocks1] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-index] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_enforce_dtypes[read_table-blocks1] 
dask/dataframe/io/tests/test_csv.py::test_enforce_columns[read_csv-blocks0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_enforce_columns[read_csv-blocks0] 
dask/dataframe/io/tests/test_csv.py::test_enforce_columns[read_table-blocks1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_enforce_columns[read_table-blocks1] 
dask/dataframe/io/tests/test_csv.py::test_read_csv[read_csv-read_csv-name,amount\nAlice,100\nBob,-200\nCharlie,300\nDennis,400\nEdith,-500\nFrank,600\nAlice,200\nFrank,-200\nBob,600\nAlice,400\nFrank,200\nAlice,300\nEdith,600-,] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv[read_csv-read_csv-name,amount\nAlice,100\nBob,-200\nCharlie,300\nDennis,400\nEdith,-500\nFrank,600\nAlice,200\nFrank,-200\nBob,600\nAlice,400\nFrank,200\nAlice,300\nEdith,600-,] 
dask/dataframe/io/tests/test_csv.py::test_read_csv[read_table-read_table-name\tamount\nAlice\t100\nBob\t-200\nCharlie\t300\nDennis\t400\nEdith\t-500\nFrank\t600\nAlice\t200\nFrank\t-200\nBob\t600\nAlice\t400\nFrank\t200\nAlice\t300\nEdith\t600-\t] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv[read_table-read_table-name\tamount\nAlice\t100\nBob\t-200\nCharlie\t300\nDennis\t400\nEdith\t-500\nFrank\t600\nAlice\t200\nFrank\t-200\nBob\t600\nAlice\t400\nFrank\t200\nAlice\t300\nEdith\t600-\t] 
dask/dataframe/io/tests/test_csv.py::test_read_csv[read_table-read_table-name   amount\nAlice    100\nBob     -200\nCharlie  300\nDennis   400\nEdith   -500\nFrank    600\nAlice    200\nFrank   -200\nBob      600\nAlice    400\nFrank    200\nAlice    300\nEdith    600-\\s+] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv[read_table-read_table-name   amount\nAlice    100\nBob     -200\nCharlie  300\nDennis   400\nEdith   -500\nFrank    600\nAlice    200\nFrank   -200\nBob      600\nAlice    400\nFrank    200\nAlice    300\nEdith    600-\\s+] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_convert_string_config 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_convert_string_config 
dask/dataframe/io/tests/test_csv.py::test_read_csv_large_skiprows[read_csv-read_csv-name,amount\nAlice,100\nBob,-200\nCharlie,300\nDennis,400\nEdith,-500\nFrank,600\nAlice,200\nFrank,-200\nBob,600\nAlice,400\nFrank,200\nAlice,300\nEdith,600-7] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_large_skiprows[read_csv-read_csv-name,amount\nAlice,100\nBob,-200\nCharlie,300\nDennis,400\nEdith,-500\nFrank,600\nAlice,200\nFrank,-200\nBob,600\nAlice,400\nFrank,200\nAlice,300\nEdith,600-7] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_large_skiprows[read_table-read_table-name\tamount\nAlice\t100\nBob\t-200\nCharlie\t300\nDennis\t400\nEdith\t-500\nFrank\t600\nAlice\t200\nFrank\t-200\nBob\t600\nAlice\t400\nFrank\t200\nAlice\t300\nEdith\t600-skip1] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_large_skiprows[read_table-read_table-name\tamount\nAlice\t100\nBob\t-200\nCharlie\t300\nDennis\t400\nEdith\t-500\nFrank\t600\nAlice\t200\nFrank\t-200\nBob\t600\nAlice\t400\nFrank\t200\nAlice\t300\nEdith\t600-skip1] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_skiprows_only_in_first_partition[read_csv-read_csv-name,amount\nAlice,100\nBob,-200\nCharlie,300\nDennis,400\nEdith,-500\nFrank,600\nAlice,200\nFrank,-200\nBob,600\nAlice,400\nFrank,200\nAlice,300\nEdith,600-7] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-index] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_skiprows_only_in_first_partition[read_csv-read_csv-name,amount\nAlice,100\nBob,-200\nCharlie,300\nDennis,400\nEdith,-500\nFrank,600\nAlice,200\nFrank,-200\nBob,600\nAlice,400\nFrank,200\nAlice,300\nEdith,600-7] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-0] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_skiprows_only_in_first_partition[read_table-read_table-name\tamount\nAlice\t100\nBob\t-200\nCharlie\t300\nDennis\t400\nEdith\t-500\nFrank\t600\nAlice\t200\nFrank\t-200\nBob\t600\nAlice\t400\nFrank\t200\nAlice\t300\nEdith\t600-skip1] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_skiprows_only_in_first_partition[read_table-read_table-name\tamount\nAlice\t100\nBob\t-200\nCharlie\t300\nDennis\t400\nEdith\t-500\nFrank\t600\nAlice\t200\nFrank\t-200\nBob\t600\nAlice\t400\nFrank\t200\nAlice\t300\nEdith\t600-skip1] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_files[read_csv-read_csv-files0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_files[read_csv-read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_files[read_table-read_table-files1] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-columns] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_files[read_table-read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_files_list[read_csv-read_csv-files0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_files_list[read_csv-read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_files_list[read_table-read_table-files1] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_files_list[read_table-read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column[read_csv-files0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column[read_table-files1] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-index] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_as_str[read_csv-files0] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_as_str[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_as_str[read_table-files1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_as_str[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_with_duplicate_name[read_csv-files0] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_with_duplicate_name[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_with_duplicate_name[read_table-files1] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_with_duplicate_name[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_is_dtype_category[read_csv-files0] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-columns] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_is_dtype_category[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_is_dtype_category[read_table-files1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_is_dtype_category[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_with_multiple_partitions_per_file[read_csv-files0] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_with_multiple_partitions_per_file[read_csv-files0] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_with_multiple_partitions_per_file[read_table-files1] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-index] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_with_multiple_partitions_per_file[read_table-files1] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_index 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-0] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-columns] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_index 
dask/dataframe/io/tests/test_csv.py::test_read_csv_skiprows_range 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_skiprows_range 
dask/dataframe/io/tests/test_csv.py::test_usecols 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_usecols 
dask/dataframe/io/tests/test_csv.py::test_string_blocksize 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_string_blocksize 
dask/dataframe/io/tests/test_csv.py::test_skipinitialspace 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-1] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_skipinitialspace 
dask/dataframe/io/tests/test_csv.py::test_consistent_dtypes 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_consistent_dtypes 
dask/dataframe/io/tests/test_csv.py::test_consistent_dtypes_2 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_consistent_dtypes_2 
dask/dataframe/io/tests/test_csv.py::test_categorical_dtypes 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-index] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_categorical_dtypes 
dask/dataframe/io/tests/test_csv.py::test_categorical_known 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_categorical_known 
dask/dataframe/io/tests/test_csv.py::test_compression_multiple_files[infer] 
[gw0] [ 53%] SKIPPED dask/dataframe/io/tests/test_csv.py::test_compression_multiple_files[infer] 
dask/dataframe/io/tests/test_csv.py::test_compression_multiple_files[gzip] 
[gw0] [ 53%] SKIPPED dask/dataframe/io/tests/test_csv.py::test_compression_multiple_files[gzip] 
dask/dataframe/io/tests/test_csv.py::test_empty_csv_file 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_empty_csv_file 
dask/dataframe/io/tests/test_csv.py::test_read_csv_no_sample 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_no_sample 
dask/dataframe/io/tests/test_csv.py::test_read_csv_sensitive_to_enforce 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_sensitive_to_enforce 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None0-None] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-columns] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None0-None] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None0-10] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None0-10] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[zip-None] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-1] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[zip-None] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[zip-10] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[zip-10] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[bz2-None] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[bz2-None] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[bz2-10] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[bz2-10] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[gzip-None] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-index] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[gzip-None] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[gzip-10] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[gzip-10] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[lzma-None] 
[gw0] [ 53%] SKIPPED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[lzma-None] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[lzma-10] 
[gw0] [ 53%] SKIPPED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[lzma-10] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[xz-None] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-0] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[xz-None] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[xz-10] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[xz-10] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[snappy-None] 
[gw0] [ 53%] SKIPPED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[snappy-None] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[snappy-10] 
[gw0] [ 53%] SKIPPED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[snappy-10] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None1-None] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-columns] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None1-None] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None1-10] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None1-10] 
dask/dataframe/io/tests/test_csv.py::test_warn_non_seekable_files 
[gw0] [ 53%] SKIPPED dask/dataframe/io/tests/test_csv.py::test_warn_non_seekable_files 
dask/dataframe/io/tests/test_csv.py::test_windows_line_terminator 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_windows_line_terminator 
dask/dataframe/io/tests/test_csv.py::test_header_int[1] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_header_int[1] 
dask/dataframe/io/tests/test_csv.py::test_header_int[2] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_header_int[2] 
dask/dataframe/io/tests/test_csv.py::test_header_int[3] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-1] 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-index] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_header_int[3] 
dask/dataframe/io/tests/test_csv.py::test_header_None 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_header_None 
dask/dataframe/io/tests/test_csv.py::test_auto_blocksize 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_auto_blocksize 
dask/dataframe/io/tests/test_csv.py::test__infer_block_size 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test__infer_block_size 
dask/dataframe/io/tests/test_csv.py::test_auto_blocksize_max64mb 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_auto_blocksize_max64mb 
dask/dataframe/io/tests/test_csv.py::test_auto_blocksize_csv 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_auto_blocksize_csv 
dask/dataframe/io/tests/test_csv.py::test_head_partial_line_fix 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_head_partial_line_fix 
dask/dataframe/io/tests/test_csv.py::test_read_csv_raises_on_no_files 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_raises_on_no_files 
dask/dataframe/io/tests/test_csv.py::test_read_csv_has_deterministic_name 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_has_deterministic_name 
dask/dataframe/io/tests/test_csv.py::test_multiple_read_csv_has_deterministic_name 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_multiple_read_csv_has_deterministic_name 
dask/dataframe/io/tests/test_csv.py::test_read_csv_has_different_names_based_on_blocksize 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_has_different_names_based_on_blocksize 
dask/dataframe/io/tests/test_csv.py::test_csv_with_integer_names 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_csv_with_integer_names 
dask/dataframe/io/tests/test_csv.py::test_late_dtypes 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-0] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_late_dtypes 
dask/dataframe/io/tests/test_csv.py::test_assume_missing 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_assume_missing 
dask/dataframe/io/tests/test_csv.py::test_index_col 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-columns] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_index_col 
dask/dataframe/io/tests/test_csv.py::test_read_csv_with_datetime_index_partitions_one 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_with_datetime_index_partitions_one 
dask/dataframe/io/tests/test_csv.py::test_read_csv_with_datetime_index_partitions_n 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_with_datetime_index_partitions_n 
dask/dataframe/io/tests/test_csv.py::test_encoding_gh601[utf-8-sig] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_encoding_gh601[utf-8-sig] 
dask/dataframe/io/tests/test_csv.py::test_encoding_gh601[utf-16] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_encoding_gh601[utf-16] 
dask/dataframe/io/tests/test_csv.py::test_encoding_gh601[utf-16-le] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_encoding_gh601[utf-16-le] 
dask/dataframe/io/tests/test_csv.py::test_encoding_gh601[utf-16-be] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_encoding_gh601[utf-16-be] 
dask/dataframe/io/tests/test_csv.py::test_read_csv_header_issue_823 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_header_issue_823 
dask/dataframe/io/tests/test_csv.py::test_none_usecols 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_none_usecols 
dask/dataframe/io/tests/test_csv.py::test_parse_dates_multi_column 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_parse_dates_multi_column 
dask/dataframe/io/tests/test_csv.py::test_read_csv_sep 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_sep 
dask/dataframe/io/tests/test_csv.py::test_read_csv_slash_r 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-1] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_slash_r 
dask/dataframe/io/tests/test_csv.py::test_read_csv_singleton_dtype 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_singleton_dtype 
dask/dataframe/io/tests/test_csv.py::test_read_csv_arrow_engine 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_arrow_engine 
dask/dataframe/io/tests/test_csv.py::test_robust_column_mismatch 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-index] 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_robust_column_mismatch 
dask/dataframe/io/tests/test_csv.py::test_different_columns_are_allowed 
[gw0] [ 53%] FAILED dask/dataframe/io/tests/test_csv.py::test_different_columns_are_allowed 
dask/dataframe/io/tests/test_csv.py::test_error_if_sample_is_too_small 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_error_if_sample_is_too_small 
dask/dataframe/io/tests/test_csv.py::test_read_csv_names_not_none 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_names_not_none 
dask/dataframe/io/tests/test_csv.py::test_to_csv 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-0] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv 
dask/dataframe/io/tests/test_csv.py::test_to_csv_multiple_files_cornercases 
[gw1] [ 53%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-columns] 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_multiple_files_cornercases 
dask/dataframe/io/tests/test_csv.py::test_to_single_csv 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_single_csv 
dask/dataframe/io/tests/test_csv.py::test_to_single_csv_with_name_function 
[gw0] [ 53%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_single_csv_with_name_function 
dask/dataframe/io/tests/test_csv.py::test_to_single_csv_with_header_first_partition_only 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_single_csv_with_header_first_partition_only 
dask/dataframe/io/tests/test_csv.py::test_to_csv_with_single_file_and_exclusive_mode 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-columns] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_with_single_file_and_exclusive_mode 
dask/dataframe/io/tests/test_csv.py::test_to_csv_single_file_exlusive_mode_no_overwrite 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-1] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_single_file_exlusive_mode_no_overwrite 
dask/dataframe/io/tests/test_csv.py::test_to_single_csv_gzip 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_single_csv_gzip 
dask/dataframe/io/tests/test_csv.py::test_to_csv_gzip 
[gw0] [ 54%] XFAIL dask/dataframe/io/tests/test_csv.py::test_to_csv_gzip 
dask/dataframe/io/tests/test_csv.py::test_to_csv_nodir 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_nodir 
dask/dataframe/io/tests/test_csv.py::test_to_csv_simple 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-index] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_simple 
dask/dataframe/io/tests/test_csv.py::test_to_csv_with_single_file_and_append_mode 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_with_single_file_and_append_mode 
dask/dataframe/io/tests/test_csv.py::test_to_csv_series 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_series 
dask/dataframe/io/tests/test_csv.py::test_to_csv_with_get 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-0] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-columns] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-1] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-index] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-0] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-columns] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_with_get 
dask/dataframe/io/tests/test_csv.py::test_to_csv_warns_using_scheduler_argument 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-1] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-index] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-0] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-columns] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-1] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-index] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_warns_using_scheduler_argument 
dask/dataframe/io/tests/test_csv.py::test_to_csv_errors_using_multiple_scheduler_args 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-0] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-columns] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-1] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-index] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-0] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_errors_using_multiple_scheduler_args 
dask/dataframe/io/tests/test_csv.py::test_to_csv_keeps_all_non_scheduler_compute_kwargs 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-columns] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-1] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-index] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-0] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-columns] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-1] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-index] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_keeps_all_non_scheduler_compute_kwargs 
dask/dataframe/io/tests/test_csv.py::test_to_csv_paths 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_paths 
dask/dataframe/io/tests/test_csv.py::test_to_csv_header_empty_dataframe[False-] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_header_empty_dataframe[False-] 
dask/dataframe/io/tests/test_csv.py::test_to_csv_header_empty_dataframe[True-x,y\n] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_header_empty_dataframe[True-x,y\n] 
dask/dataframe/io/tests/test_csv.py::test_to_csv_header[False-False-a,1\n-d,4\n] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_header[False-False-a,1\n-d,4\n] 
dask/dataframe/io/tests/test_csv.py::test_to_csv_header[True-False-x,y\n-x,y\n] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_header[True-False-x,y\n-x,y\n] 
dask/dataframe/io/tests/test_csv.py::test_to_csv_header[False-True-a,1\n-d,4\n] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_header[False-True-a,1\n-d,4\n] 
dask/dataframe/io/tests/test_csv.py::test_to_csv_header[True-True-x,y\n-d,4\n] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_header[True-True-x,y\n-d,4\n] 
dask/dataframe/io/tests/test_csv.py::test_to_csv_header[header4-False-aa,bb\n-aa,bb\n] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-0] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_header[header4-False-aa,bb\n-aa,bb\n] 
dask/dataframe/io/tests/test_csv.py::test_to_csv_header[header5-True-aa,bb\n-d,4\n] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_header[header5-True-aa,bb\n-d,4\n] 
dask/dataframe/io/tests/test_csv.py::test_to_csv_line_ending 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_to_csv_line_ending 
dask/dataframe/io/tests/test_csv.py::test_block_mask[block_lists0] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_block_mask[block_lists0] 
dask/dataframe/io/tests/test_csv.py::test_block_mask[block_lists1] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_block_mask[block_lists1] 
dask/dataframe/io/tests/test_csv.py::test_block_mask[block_lists2] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_block_mask[block_lists2] 
dask/dataframe/io/tests/test_csv.py::test_block_mask[block_lists3] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_block_mask[block_lists3] 
dask/dataframe/io/tests/test_csv.py::test_reading_empty_csv_files_with_path 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-columns] 
[gw0] [ 54%] FAILED dask/dataframe/io/tests/test_csv.py::test_reading_empty_csv_files_with_path 
dask/dataframe/io/tests/test_csv.py::test_read_csv_groupby_get_group 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_read_csv_groupby_get_group 
dask/dataframe/io/tests/test_csv.py::test_csv_getitem_column_order 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_csv_getitem_column_order 
dask/dataframe/io/tests/test_csv.py::test_csv_parse_fail 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_csv_parse_fail 
dask/dataframe/io/tests/test_csv.py::test_csv_name_should_be_different_even_if_head_is_same 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_csv_name_should_be_different_even_if_head_is_same 
dask/dataframe/io/tests/test_csv.py::test_select_with_include_path_column 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_select_with_include_path_column 
dask/dataframe/io/tests/test_csv.py::test_names_with_header_0[True] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_names_with_header_0[True] 
dask/dataframe/io/tests/test_csv.py::test_names_with_header_0[False] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_csv.py::test_names_with_header_0[False] 
dask/dataframe/io/tests/test_demo.py::test_make_timeseries 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-1] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_make_timeseries 
dask/dataframe/io/tests/test_demo.py::test_make_timeseries_no_args 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_make_timeseries_no_args 
dask/dataframe/io/tests/test_demo.py::test_no_overlaps 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_no_overlaps 
dask/dataframe/io/tests/test_demo.py::test_make_timeseries_keywords 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_make_timeseries_keywords 
dask/dataframe/io/tests/test_demo.py::test_make_timeseries_fancy_keywords 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_make_timeseries_fancy_keywords 
dask/dataframe/io/tests/test_demo.py::test_make_timeseries_getitem_compute 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-index] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-0] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-columns] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-1] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-index] 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-0] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_make_timeseries_getitem_compute 
dask/dataframe/io/tests/test_demo.py::test_make_timeseries_column_projection 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-columns] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_make_timeseries_column_projection 
dask/dataframe/io/tests/test_demo.py::test_with_spec[None] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_with_spec[None] 
dask/dataframe/io/tests/test_demo.py::test_with_spec[42] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_with_spec[42] 
dask/dataframe/io/tests/test_demo.py::test_with_spec_non_default[None] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_with_spec_non_default[None] 
dask/dataframe/io/tests/test_demo.py::test_with_spec_non_default[42] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_with_spec_non_default[42] 
dask/dataframe/io/tests/test_demo.py::test_with_spec_pyarrow 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_with_spec_pyarrow 
dask/dataframe/io/tests/test_demo.py::test_same_prefix_col_numbering[None] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_same_prefix_col_numbering[None] 
dask/dataframe/io/tests/test_demo.py::test_same_prefix_col_numbering[42] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_same_prefix_col_numbering[42] 
dask/dataframe/io/tests/test_demo.py::test_with_spec_category_nunique 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_with_spec_category_nunique 
dask/dataframe/io/tests/test_demo.py::test_with_spec_default_integer[None] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_with_spec_default_integer[None] 
dask/dataframe/io/tests/test_demo.py::test_with_spec_default_integer[42] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_with_spec_default_integer[42] 
dask/dataframe/io/tests/test_demo.py::test_with_spec_integer_method 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_with_spec_integer_method 
dask/dataframe/io/tests/test_demo.py::test_with_spec_datetime_index 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_demo.py::test_with_spec_datetime_index 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_multiple_nodes 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_multiple_nodes 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_multiple_files 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_multiple_files 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_modes_multiple_nodes 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_modes_multiple_nodes 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_modes_multiple_files 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_modes_multiple_files 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_lock_delays 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_lock_delays 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_exceptions 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_exceptions 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[1-sync] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[1-sync] 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[1-threads] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[1-threads] 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[1-processes] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[1-processes] 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[4-sync] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[4-sync] 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[4-threads] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[4-threads] 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[4-processes] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[4-processes] 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[10-sync] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[10-sync] 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[10-threads] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[10-threads] 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[10-processes] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_schedulers[10-processes] 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_kwargs 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_kwargs 
dask/dataframe/io/tests/test_hdf.py::test_to_fmt_warns 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_fmt_warns 
dask/dataframe/io/tests/test_hdf.py::test_read_hdf[data0-assert_frame_equal] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_read_hdf[data0-assert_frame_equal] 
dask/dataframe/io/tests/test_hdf.py::test_read_hdf[data1-assert_series_equal] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_read_hdf[data1-assert_series_equal] 
dask/dataframe/io/tests/test_hdf.py::test_read_hdf_multiply_open 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_read_hdf_multiply_open 
dask/dataframe/io/tests/test_hdf.py::test_read_hdf_multiple 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_read_hdf_multiple 
dask/dataframe/io/tests/test_hdf.py::test_read_hdf_start_stop_values 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_read_hdf_start_stop_values 
dask/dataframe/io/tests/test_hdf.py::test_hdf_globbing 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_hdf_globbing 
dask/dataframe/io/tests/test_hdf.py::test_hdf_file_list 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_hdf_file_list 
dask/dataframe/io/tests/test_hdf.py::test_read_hdf_pattern_pathlike 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_read_hdf_pattern_pathlike 
dask/dataframe/io/tests/test_hdf.py::test_to_hdf_path_pathlike 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_to_hdf_path_pathlike 
dask/dataframe/io/tests/test_hdf.py::test_read_hdf_doesnt_segfault 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_read_hdf_doesnt_segfault 
dask/dataframe/io/tests/test_hdf.py::test_hdf_filenames 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_hdf_filenames 
dask/dataframe/io/tests/test_hdf.py::test_hdf_path_exceptions 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_hdf_path_exceptions 
dask/dataframe/io/tests/test_hdf.py::test_hdf_nonpandas_keys 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_hdf_nonpandas_keys 
dask/dataframe/io/tests/test_hdf.py::test_hdf_empty_dataframe 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_hdf.py::test_hdf_empty_dataframe 
dask/dataframe/io/tests/test_io.py::test_meta_from_array 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_meta_from_array 
dask/dataframe/io/tests/test_io.py::test_meta_from_1darray 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_meta_from_1darray 
dask/dataframe/io/tests/test_io.py::test_meta_from_recarray 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_meta_from_recarray 
dask/dataframe/io/tests/test_io.py::test_from_array 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-columns] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_array 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-1] 
dask/dataframe/io/tests/test_io.py::test_from_array_with_record_dtype 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_array_with_record_dtype 
dask/dataframe/io/tests/test_io.py::test_from_pandas_dataframe 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_dataframe 
dask/dataframe/io/tests/test_io.py::test_from_pandas_small 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_small 
dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_is_accurate[1] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_is_accurate[1] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_is_accurate[2] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_is_accurate[2] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_is_accurate[4] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_is_accurate[4] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_is_accurate[5] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_is_accurate[5] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_series 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_series 
dask/dataframe/io/tests/test_io.py::test_from_pandas_non_sorted 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_non_sorted 
dask/dataframe/io/tests/test_io.py::test_from_pandas_single_row 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_single_row 
dask/dataframe/io/tests/test_io.py::test_from_pandas_with_datetime_index 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_with_datetime_index 
dask/dataframe/io/tests/test_io.py::test_from_pandas_with_index_nulls[None] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_with_index_nulls[None] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_with_index_nulls[null_value1] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_with_index_nulls[null_value1] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_with_index_nulls[null_value2] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_with_index_nulls[null_value2] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_with_wrong_args 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-1] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_with_wrong_args 
dask/dataframe/io/tests/test_io.py::test_from_pandas_chunksize_one 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-index] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_chunksize_one 
dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_duplicates[index0] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_duplicates[index0] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_duplicates[index1] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_duplicates[index1] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_duplicates[index2] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_npartitions_duplicates[index2] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_convert_string_config 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_convert_string_config 
dask/dataframe/io/tests/test_io.py::test_from_pandas_immutable[True-index0] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_immutable[True-index0] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_immutable[True-index1] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_immutable[True-index1] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_immutable[False-index0] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_immutable[False-index0] 
dask/dataframe/io/tests/test_io.py::test_from_pandas_immutable[False-index1] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_pandas_immutable[False-index1] 
dask/dataframe/io/tests/test_io.py::test_gpu_from_pandas_npartitions_duplicates 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_io.py::test_gpu_from_pandas_npartitions_duplicates 
dask/dataframe/io/tests/test_io.py::test_DataFrame_from_dask_array 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_DataFrame_from_dask_array 
dask/dataframe/io/tests/test_io.py::test_DataFrame_from_dask_array_with_blockwise_ops 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_DataFrame_from_dask_array_with_blockwise_ops 
dask/dataframe/io/tests/test_io.py::test_Series_from_dask_array 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-0] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_Series_from_dask_array 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_index[True] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_index[True] 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_index[False] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_index[False] 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_index_raises 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_index_raises 
dask/dataframe/io/tests/test_io.py::test_from_array_raises_more_than_2D 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_array_raises_more_than_2D 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_compat_numpy_array 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_compat_numpy_array 
dask/dataframe/io/tests/test_io.py::test_from_array_wrong_column_shape_error 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_array_wrong_column_shape_error 
dask/dataframe/io/tests/test_io.py::test_from_array_with_column_names 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_array_with_column_names 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_compat_numpy_array_1d 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_compat_numpy_array_1d 
dask/dataframe/io/tests/test_io.py::test_from_array_1d_with_column_names 
[gw1] [ 54%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-columns] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_array_1d_with_column_names 
dask/dataframe/io/tests/test_io.py::test_from_array_1d_list_of_columns_gives_dataframe 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_array_1d_list_of_columns_gives_dataframe 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_struct_dtype 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_struct_dtype 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_unknown_chunks 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_unknown_chunks 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_empty_chunks[chunksizes0-expected_divisions0] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_empty_chunks[chunksizes0-expected_divisions0] 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_empty_chunks[chunksizes1-expected_divisions1] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_empty_chunks[chunksizes1-expected_divisions1] 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_empty_chunks[chunksizes2-expected_divisions2] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_empty_chunks[chunksizes2-expected_divisions2] 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_unknown_width_error 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_unknown_width_error 
dask/dataframe/io/tests/test_io.py::test_from_array_dispatching[cupy-cudf] 
[gw0] [ 54%] SKIPPED dask/dataframe/io/tests/test_io.py::test_from_array_dispatching[cupy-cudf] 
dask/dataframe/io/tests/test_io.py::test_from_array_dispatching[numpy-pandas] 
[gw0] [ 54%] PASSED dask/dataframe/io/tests/test_io.py::test_from_array_dispatching[numpy-pandas] 
dask/dataframe/io/tests/test_io.py::test_to_bag 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-1] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-index] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-0] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-columns] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-columns] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-1] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-index] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-0] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>0] 
[gw1] [ 55%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>1] 
[gw1] [ 55%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>2] 
[gw1] [ 55%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>3] 
[gw1] [ 55%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>3] 
dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>4] 
[gw1] [ 55%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>4] 
dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>5] 
[gw1] [ 55%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_unary_operators[<lambda>5] 
dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>0] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>1] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>2] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>3] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>3] 
dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>4] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>4] 
dask/dataframe/dask_expr/tests/test_collection.py::test_and_or[<lambda>0] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_and_or[<lambda>0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_and_or[<lambda>1] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_and_or[<lambda>1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_to_timestamp[start] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_to_timestamp[start] 
dask/dataframe/dask_expr/tests/test_collection.py::test_to_timestamp[end] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_to_timestamp[end] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>0] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>1] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>2] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>3] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>3] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>4] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>4] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>5] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>5] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>6] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>6] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>7] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>7] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>8] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>8] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>9] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>9] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>10] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>10] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>11] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>11] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>12] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>12] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>13] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>13] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>14] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>14] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>15] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>15] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>16] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>16] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>17] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>17] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>18] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>18] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>19] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>19] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>20] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>20] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>21] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>21] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>22] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>22] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>23] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>23] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>24] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>24] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>25] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>25] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>26] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>26] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>27] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>27] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>28] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>28] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>29] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>29] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>30] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_to_bag 
dask/dataframe/io/tests/test_io.py::test_to_bag_frame 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_to_bag_frame 
dask/dataframe/io/tests/test_io.py::test_to_records 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_to_records 
dask/dataframe/io/tests/test_io.py::test_to_records_with_lengths[lengths0] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_to_records_with_lengths[lengths0] 
dask/dataframe/io/tests/test_io.py::test_to_records_with_lengths[True] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_to_records_with_lengths[True] 
dask/dataframe/io/tests/test_io.py::test_to_records_raises 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_to_records_raises 
dask/dataframe/io/tests/test_io.py::test_from_delayed 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>30] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>31] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_delayed 
dask/dataframe/io/tests/test_io.py::test_from_delayed_to_dask_array 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_delayed_to_dask_array 
dask/dataframe/io/tests/test_io.py::test_from_delayed_misordered_meta 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_delayed_misordered_meta 
dask/dataframe/io/tests/test_io.py::test_to_delayed 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_to_delayed 
dask/dataframe/io/tests/test_io.py::test_to_delayed_optimize_graph 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_to_delayed_optimize_graph 
dask/dataframe/io/tests/test_io.py::test_from_dask_array_index_dtype 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>31] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>32] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dask_array_index_dtype 
dask/dataframe/io/tests/test_io.py::test_from_map_simple[vals0] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_map_simple[vals0] 
dask/dataframe/io/tests/test_io.py::test_from_map_simple[vals1] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_map_simple[vals1] 
dask/dataframe/io/tests/test_io.py::test_from_map_simple[vals2] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_map_simple[vals2] 
dask/dataframe/io/tests/test_io.py::test_from_map_multi 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_map_multi 
dask/dataframe/io/tests/test_io.py::test_from_map_args 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_map_args 
dask/dataframe/io/tests/test_io.py::test_from_map_divisions 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_map_divisions 
dask/dataframe/io/tests/test_io.py::test_from_map_meta 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_map_meta 
dask/dataframe/io/tests/test_io.py::test_from_map_other_iterables[iterable0] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_map_other_iterables[iterable0] 
dask/dataframe/io/tests/test_io.py::test_from_map_other_iterables[iterable1] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_map_other_iterables[iterable1] 
dask/dataframe/io/tests/test_io.py::test_from_map_other_iterables[_generator] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_map_other_iterables[_generator] 
dask/dataframe/io/tests/test_io.py::test_from_map_column_projection 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_map_column_projection 
dask/dataframe/io/tests/test_io.py::test_from_dict_backends[pandas] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_from_dict_backends[pandas] 
dask/dataframe/io/tests/test_io.py::test_from_dict_backends[cudf] 
[gw0] [ 55%] SKIPPED dask/dataframe/io/tests/test_io.py::test_from_dict_backends[cudf] 
dask/dataframe/io/tests/test_io.py::test_sorted_division_locations_duplicates[pandas] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_io.py::test_sorted_division_locations_duplicates[pandas] 
dask/dataframe/io/tests/test_io.py::test_sorted_division_locations_duplicates[cudf] 
[gw0] [ 55%] SKIPPED dask/dataframe/io/tests/test_io.py::test_sorted_division_locations_duplicates[cudf] 
dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[split] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>32] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>33] 
[gw0] [ 55%] FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[split] 
dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[records] 
[gw0] [ 55%] FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[records] 
dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[index] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>33] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>34] 
[gw0] [ 55%] FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[index] 
dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[columns] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>34] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>35] 
[gw0] [ 55%] FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[columns] 
dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[values] 
[gw0] [ 55%] FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[values] 
dask/dataframe/io/tests/test_json.py::test_read_json_path_column_with_duplicate_name_is_error 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_path_column_with_duplicate_name_is_error 
dask/dataframe/io/tests/test_json.py::test_read_json_with_path_converter 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>35] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>36] 
[gw0] [ 55%] FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_converter 
dask/dataframe/io/tests/test_json.py::test_read_orient_not_records_and_lines 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_orient_not_records_and_lines 
dask/dataframe/io/tests/test_json.py::test_write_orient_not_records_and_lines 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_write_orient_not_records_and_lines 
dask/dataframe/io/tests/test_json.py::test_read_json_multiple_files_with_path_column[5] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>36] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>37] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_multiple_files_with_path_column[5] 
dask/dataframe/io/tests/test_json.py::test_read_json_multiple_files_with_path_column[15] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_multiple_files_with_path_column[15] 
dask/dataframe/io/tests/test_json.py::test_read_json_multiple_files_with_path_column[33] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_multiple_files_with_path_column[33] 
dask/dataframe/io/tests/test_json.py::test_read_json_multiple_files_with_path_column[200] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_multiple_files_with_path_column[200] 
dask/dataframe/io/tests/test_json.py::test_read_json_multiple_files_with_path_column[90000] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_multiple_files_with_path_column[90000] 
dask/dataframe/io/tests/test_json.py::test_read_json_basic[split] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>37] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>38] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_basic[split] 
dask/dataframe/io/tests/test_json.py::test_read_json_basic[records] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_basic[records] 
dask/dataframe/io/tests/test_json.py::test_read_json_basic[index] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_basic[index] 
dask/dataframe/io/tests/test_json.py::test_read_json_basic[columns] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_basic[columns] 
dask/dataframe/io/tests/test_json.py::test_read_json_basic[values] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_basic[values] 
dask/dataframe/io/tests/test_json.py::test_read_json_fkeyword[pandas] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_fkeyword[pandas] 
dask/dataframe/io/tests/test_json.py::test_read_json_fkeyword[json] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_fkeyword[json] 
dask/dataframe/io/tests/test_json.py::test_read_json_engine_str[ujson] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_engine_str[ujson] 
dask/dataframe/io/tests/test_json.py::test_read_json_engine_str[read_json] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_engine_str[read_json] 
dask/dataframe/io/tests/test_json.py::test_read_json_meta[split] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>38] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>39] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_meta[split] 
dask/dataframe/io/tests/test_json.py::test_read_json_meta[records] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_meta[records] 
dask/dataframe/io/tests/test_json.py::test_read_json_meta[index] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_meta[index] 
dask/dataframe/io/tests/test_json.py::test_read_json_meta[columns] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_meta[columns] 
dask/dataframe/io/tests/test_json.py::test_read_json_meta[values] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_meta[values] 
dask/dataframe/io/tests/test_json.py::test_write_json_basic[split] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_write_json_basic[split] 
dask/dataframe/io/tests/test_json.py::test_write_json_basic[records] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_write_json_basic[records] 
dask/dataframe/io/tests/test_json.py::test_write_json_basic[index] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_write_json_basic[index] 
dask/dataframe/io/tests/test_json.py::test_write_json_basic[columns] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_write_json_basic[columns] 
dask/dataframe/io/tests/test_json.py::test_write_json_basic[values] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_write_json_basic[values] 
dask/dataframe/io/tests/test_json.py::test_to_json_with_get 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>39] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>40] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>40] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>41] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>41] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>42] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>42] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>43] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>43] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>44] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>44] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>45] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>45] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>46] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_to_json_with_get 
dask/dataframe/io/tests/test_json.py::test_read_json_error 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_error 
dask/dataframe/io/tests/test_json.py::test_read_chunked[5] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_chunked[5] 
dask/dataframe/io/tests/test_json.py::test_read_chunked[15] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_chunked[15] 
dask/dataframe/io/tests/test_json.py::test_read_chunked[33] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_chunked[33] 
dask/dataframe/io/tests/test_json.py::test_read_chunked[200] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_chunked[200] 
dask/dataframe/io/tests/test_json.py::test_read_chunked[90000] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_chunked[90000] 
dask/dataframe/io/tests/test_json.py::test_json_compressed[None] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_json_compressed[None] 
dask/dataframe/io/tests/test_json.py::test_json_compressed[gzip] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_json_compressed[gzip] 
dask/dataframe/io/tests/test_json.py::test_json_compressed[xz] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>46] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>47] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_json_compressed[xz] 
dask/dataframe/io/tests/test_json.py::test_read_json_inferred_compression 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_read_json_inferred_compression 
dask/dataframe/io/tests/test_json.py::test_to_json_results 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_json.py::test_to_json_results 
dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip[None-None] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip[None-None] 
dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip[None-i32] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip[None-i32] 
dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip[columns1-None] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip[columns1-None] 
dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip[columns1-i32] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip[columns1-i32] 
dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip_aggregate_files[True] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip_aggregate_files[True] 
dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip_aggregate_files[False] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>47] 
dask/dataframe/dask_expr/tests/test_collection.py::test_add_prefix 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip_aggregate_files[False] 
dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip_aggregate_files[2] 
[gw1] [ 55%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_add_prefix 
dask/dataframe/dask_expr/tests/test_collection.py::test_add_suffix 
[gw1] [ 55%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_add_suffix 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip_aggregate_files[2] 
dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip_aggregate_files[4] 
dask/dataframe/dask_expr/tests/test_collection.py::test_select_dtypes_projection 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_orc.py::test_orc_roundtrip_aggregate_files[4] 
dask/dataframe/io/tests/test_orc.py::test_to_orc_delayed 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_orc.py::test_to_orc_delayed 
dask/dataframe/io/tests/test_parquet.py::test_get_engine_pyarrow 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_get_engine_pyarrow 
dask/dataframe/io/tests/test_parquet.py::test_get_engine_no_engine 
[gw0] [ 55%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_get_engine_no_engine 
dask/dataframe/io/tests/test_parquet.py::test_get_engine_third_party 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_get_engine_third_party 
dask/dataframe/io/tests/test_parquet.py::test_get_engine_invalid 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_get_engine_invalid 
dask/dataframe/io/tests/test_parquet.py::test_local[pyarrow-pyarrow-False] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_local[pyarrow-pyarrow-False] 
dask/dataframe/io/tests/test_parquet.py::test_local[pyarrow-pyarrow-True] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_local[pyarrow-pyarrow-True] 
dask/dataframe/io/tests/test_parquet.py::test_empty[pyarrow-pyarrow-False] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_empty[pyarrow-pyarrow-False] 
dask/dataframe/io/tests/test_parquet.py::test_empty[pyarrow-pyarrow-True] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_empty[pyarrow-pyarrow-True] 
dask/dataframe/io/tests/test_parquet.py::test_simple[pyarrow-pyarrow] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_simple[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_delayed_no_metadata[pyarrow-pyarrow] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_select_dtypes_projection 
dask/dataframe/dask_expr/tests/test_collection.py::test_rename 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_delayed_no_metadata[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_read_glob[pyarrow-pyarrow] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_glob[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_calculate_divisions_false[pyarrow-pyarrow] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_rename 
dask/dataframe/dask_expr/tests/test_collection.py::test_mask_where 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_calculate_divisions_false[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_read_list[pyarrow-pyarrow] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_list[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_columns_auto_index[pyarrow-pyarrow] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_mask_where 
dask/dataframe/dask_expr/tests/test_collection.py::test_isna 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_isna 
dask/dataframe/dask_expr/tests/test_collection.py::test_clear_divisions 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_columns_auto_index[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_columns_index[pyarrow-pyarrow] 
[gw1] [ 55%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_clear_divisions 
dask/dataframe/dask_expr/tests/test_collection.py::test_abs_errors 
[gw1] [ 55%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_abs_errors 
dask/dataframe/dask_expr/tests/test_collection.py::test_to_datetime_timezone 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_columns_index[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_nonsense_column[pyarrow] 
[gw0] [ 55%] PASSED dask/dataframe/io/tests/test_parquet.py::test_nonsense_column[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_columns_no_index[pyarrow-pyarrow] 
[gw1] [ 56%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_to_datetime_timezone 
dask/dataframe/dask_expr/tests/test_collection.py::test_to_datetime 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_columns_no_index[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_calculate_divisions_no_index[pyarrow-pyarrow] 
[gw1] [ 56%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_to_datetime 
dask/dataframe/dask_expr/tests/test_collection.py::test_to_numeric 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_calculate_divisions_no_index[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_columns_index_with_multi_index[pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_columns_index_with_multi_index[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_no_index[pyarrow-pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_no_index[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_read_series[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_to_numeric 
dask/dataframe/dask_expr/tests/test_collection.py::test_to_timedelta 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_series[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_names[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_to_timedelta 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_not_implemented 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_names[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_from_pandas[pyarrow-pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_from_pandas[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_nullable_dtypes 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_nullable_dtypes 
dask/dataframe/io/tests/test_parquet.py::test_use_nullable_dtypes_with_types_mapper[pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_use_nullable_dtypes_with_types_mapper[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_categorical[pyarrow-pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_not_implemented 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only[<lambda>0] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only[<lambda>0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only[<lambda>1] 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_categorical[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_append[pyarrow-False] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append[pyarrow-False] 
dask/dataframe/io/tests/test_parquet.py::test_append[pyarrow-True] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only[<lambda>1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>0] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append[pyarrow-True] 
dask/dataframe/io/tests/test_parquet.py::test_append_create[pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append_create[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_append_with_partition 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>1] 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_append_with_partition 
dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats[pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats_pyarrow[False-False] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats_pyarrow[False-False] 
dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats_pyarrow[False-True] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>2] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats_pyarrow[False-True] 
dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats_pyarrow[True-False] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats_pyarrow[True-False] 
dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats_pyarrow[True-True] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats_pyarrow[True-True] 
dask/dataframe/io/tests/test_parquet.py::test_partition_parallel_metadata[pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_partition_parallel_metadata[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats_2[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>3] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>3] 
dask/dataframe/dask_expr/tests/test_collection.py::test_map_meta 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats_2[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_append_wo_index[pyarrow-False] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_map_meta 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_add_suffix_add_prefix 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append_wo_index[pyarrow-False] 
dask/dataframe/io/tests/test_parquet.py::test_append_wo_index[pyarrow-True] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append_wo_index[pyarrow-True] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_add_suffix_add_prefix 
dask/dataframe/io/tests/test_parquet.py::test_append_overlapping_divisions[pyarrow-index0-offset0-False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_rename_axis 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append_overlapping_divisions[pyarrow-index0-offset0-False] 
dask/dataframe/io/tests/test_parquet.py::test_append_overlapping_divisions[pyarrow-index0-offset0-True] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append_overlapping_divisions[pyarrow-index0-offset0-True] 
dask/dataframe/io/tests/test_parquet.py::test_append_overlapping_divisions[pyarrow-index1-499-False] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append_overlapping_divisions[pyarrow-index1-499-False] 
dask/dataframe/io/tests/test_parquet.py::test_append_overlapping_divisions[pyarrow-index1-499-True] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append_overlapping_divisions[pyarrow-index1-499-True] 
dask/dataframe/io/tests/test_parquet.py::test_append_known_divisions_to_unknown_divisions_works[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_rename_axis 
dask/dataframe/dask_expr/tests/test_collection.py::test_series_name 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append_known_divisions_to_unknown_divisions_works[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_append_different_columns[pyarrow-False] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append_different_columns[pyarrow-False] 
dask/dataframe/io/tests/test_parquet.py::test_append_different_columns[pyarrow-True] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_series_name 
dask/dataframe/dask_expr/tests/test_collection.py::test_isin 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_append_different_columns[pyarrow-True] 
dask/dataframe/io/tests/test_parquet.py::test_append_dict_column 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_append_dict_column 
dask/dataframe/io/tests/test_parquet.py::test_filter_with_struct_column 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_filter_with_struct_column 
dask/dataframe/io/tests/test_parquet.py::test_ordering[pyarrow-pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_ordering[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_read_parquet_custom_columns[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_isin 
dask/dataframe/dask_expr/tests/test_collection.py::test_isin_repr 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_isin_repr 
dask/dataframe/dask_expr/tests/test_collection.py::test_round 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_parquet_custom_columns[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df0-write_kwargs0-read_kwargs0] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df0-write_kwargs0-read_kwargs0] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df1-write_kwargs1-read_kwargs1] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df1-write_kwargs1-read_kwargs1] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df2-write_kwargs2-read_kwargs2] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df2-write_kwargs2-read_kwargs2] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df3-write_kwargs3-read_kwargs3] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df3-write_kwargs3-read_kwargs3] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df4-write_kwargs4-read_kwargs4] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df4-write_kwargs4-read_kwargs4] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df5-write_kwargs5-read_kwargs5] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df5-write_kwargs5-read_kwargs5] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df6-write_kwargs6-read_kwargs6] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df6-write_kwargs6-read_kwargs6] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df7-write_kwargs7-read_kwargs7] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df7-write_kwargs7-read_kwargs7] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df8-write_kwargs8-read_kwargs8] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df8-write_kwargs8-read_kwargs8] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df9-write_kwargs9-read_kwargs9] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df9-write_kwargs9-read_kwargs9] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df10-write_kwargs10-read_kwargs10] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df10-write_kwargs10-read_kwargs10] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df11-write_kwargs11-read_kwargs11] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df11-write_kwargs11-read_kwargs11] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df12-write_kwargs12-read_kwargs12] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df12-write_kwargs12-read_kwargs12] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df13-write_kwargs13-read_kwargs13] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df13-write_kwargs13-read_kwargs13] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df14-write_kwargs14-read_kwargs14] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df14-write_kwargs14-read_kwargs14] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df15-write_kwargs15-read_kwargs15] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df15-write_kwargs15-read_kwargs15] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df16-write_kwargs16-read_kwargs16] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df16-write_kwargs16-read_kwargs16] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df17-write_kwargs17-read_kwargs17] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df17-write_kwargs17-read_kwargs17] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df18-write_kwargs18-read_kwargs18] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df18-write_kwargs18-read_kwargs18] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df19-write_kwargs19-read_kwargs19] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df19-write_kwargs19-read_kwargs19] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df20-write_kwargs20-read_kwargs20] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df20-write_kwargs20-read_kwargs20] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df21-write_kwargs21-read_kwargs21] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df21-write_kwargs21-read_kwargs21] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df22-write_kwargs22-read_kwargs22] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df22-write_kwargs22-read_kwargs22] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df23-write_kwargs23-read_kwargs23] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df23-write_kwargs23-read_kwargs23] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df24-write_kwargs24-read_kwargs24] 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_roundtrip[pyarrow-df24-write_kwargs24-read_kwargs24] 
dask/dataframe/io/tests/test_parquet.py::test_categories[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_round 
dask/dataframe/dask_expr/tests/test_collection.py::test_repr 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repr 
dask/dataframe/dask_expr/tests/test_collection.py::test_combine_first_simplify 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_categories[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_categories_unnamed_index[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_combine_first_simplify 
dask/dataframe/dask_expr/tests/test_collection.py::test_rename_traverse_filter 
[gw0] [ 56%] XFAIL dask/dataframe/io/tests/test_parquet.py::test_categories_unnamed_index[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_empty_partition[pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_empty_partition[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_timestamp_index[pyarrow-True] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_rename_traverse_filter 
dask/dataframe/dask_expr/tests/test_collection.py::test_columns_traverse_filters 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_timestamp_index[pyarrow-True] 
dask/dataframe/io/tests/test_parquet.py::test_timestamp_index[pyarrow-False] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_timestamp_index[pyarrow-False] 
dask/dataframe/io/tests/test_parquet.py::test_to_parquet_pyarrow_w_inconsistent_schema_by_partition_succeeds_w_manual_schema 
[gw0] [ 56%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_to_parquet_pyarrow_w_inconsistent_schema_by_partition_succeeds_w_manual_schema 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_inference[infer-False] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_inference[infer-False] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_inference[infer-True] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_columns_traverse_filters 
dask/dataframe/dask_expr/tests/test_collection.py::test_clip_traverse_filters 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_clip_traverse_filters 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-zz0] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-zz0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-projection1] 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_inference[infer-True] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_inference[complex-False] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_inference[complex-False] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_inference[complex-True] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-projection1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-projection2] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-projection2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-zz1] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-zz1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-zz0] 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_inference[complex-True] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_mismatch_error 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_mismatch_error 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_mismatch_explicit_schema_none 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_mismatch_explicit_schema_none 
dask/dataframe/io/tests/test_parquet.py::test_partition_on[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-zz0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-projection1] 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_partition_on[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_partition_on_duplicates[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-projection1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-projection2] 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_partition_on_duplicates[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_partition_on_string[aa] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-projection2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-zz1] 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_partition_on_string[aa] 
dask/dataframe/io/tests/test_parquet.py::test_partition_on_string[partition_on1] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-zz1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_rename_columns 
[gw1] [ 56%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_rename_columns 
dask/dataframe/dask_expr/tests/test_collection.py::test_columns_named_divisions_and_meta 
[gw1] [ 56%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_columns_named_divisions_and_meta 
dask/dataframe/dask_expr/tests/test_collection.py::test_broadcast 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_partition_on_string[partition_on1] 
dask/dataframe/io/tests/test_parquet.py::test_filters_categorical[pyarrow-pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_broadcast 
dask/dataframe/dask_expr/tests/test_collection.py::test_persist 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_persist 
dask/dataframe/dask_expr/tests/test_collection.py::test_index 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_index 
dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index[True] 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_filters_categorical[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_filters[pyarrow-pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_filters[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_filters_v0[pyarrow-pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index[True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index[False] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_filters_v0[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_filtering_pyarrow_dataset[pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_filtering_pyarrow_dataset[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_filters_file_list[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index[False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_head 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_filters_file_list[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_filter_divisions 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_filter_divisions 
dask/dataframe/io/tests/test_parquet.py::test_to_parquet_lazy[pyarrow-threads] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_to_parquet_lazy[pyarrow-threads] 
dask/dataframe/io/tests/test_parquet.py::test_to_parquet_lazy[pyarrow-processes] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head 
dask/dataframe/dask_expr/tests/test_collection.py::test_head_down 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head_down 
dask/dataframe/dask_expr/tests/test_collection.py::test_case_when 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_case_when 
dask/dataframe/dask_expr/tests/test_collection.py::test_head_head 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head_head 
dask/dataframe/dask_expr/tests/test_collection.py::test_head_npartitions 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head_npartitions 
dask/dataframe/dask_expr/tests/test_collection.py::test_head_tail_repartition 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head_tail_repartition 
dask/dataframe/dask_expr/tests/test_collection.py::test_tail 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_to_parquet_lazy[pyarrow-processes] 
dask/dataframe/io/tests/test_parquet.py::test_to_parquet_calls_invalidate_cache[False] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_to_parquet_calls_invalidate_cache[False] 
dask/dataframe/io/tests/test_parquet.py::test_to_parquet_calls_invalidate_cache[True] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_to_parquet_calls_invalidate_cache[True] 
dask/dataframe/io/tests/test_parquet.py::test_parquet_select_cats[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_tail 
dask/dataframe/dask_expr/tests/test_collection.py::test_tail_down 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_tail_down 
dask/dataframe/dask_expr/tests/test_collection.py::test_tail_tail 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_tail_tail 
dask/dataframe/dask_expr/tests/test_collection.py::test_tail_repartition 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_parquet_select_cats[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_columns_name[pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_columns_name[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_compression[pyarrow-None] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_compression[pyarrow-None] 
dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_compression[pyarrow-gzip] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_tail_repartition 
dask/dataframe/dask_expr/tests/test_collection.py::test_projection_stacking 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_compression[pyarrow-gzip] 
dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_compression[pyarrow-snappy] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_compression[pyarrow-snappy] 
dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_partition_on_and_compression[pyarrow-None] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_partition_on_and_compression[pyarrow-None] 
dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_partition_on_and_compression[pyarrow-gzip] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_partition_on_and_compression[pyarrow-gzip] 
dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_partition_on_and_compression[pyarrow-snappy] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_partition_on_and_compression[pyarrow-snappy] 
dask/dataframe/io/tests/test_parquet.py::test_read_no_metadata[pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_no_metadata[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_kwargs[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_projection_stacking 
dask/dataframe/dask_expr/tests/test_collection.py::test_projection_stacking_coercion 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_projection_stacking_coercion 
dask/dataframe/dask_expr/tests/test_collection.py::test_projection_pushdown_dim_0 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_kwargs[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_unknown_kwargs[pyarrow] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_unknown_kwargs[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_to_parquet_with_get[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_projection_pushdown_dim_0 
dask/dataframe/dask_expr/tests/test_collection.py::test_remove_unnecessary_projections 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_remove_unnecessary_projections 
dask/dataframe/dask_expr/tests/test_collection.py::test_substitute 
[gw1] [ 56%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_substitute 
dask/dataframe/dask_expr/tests/test_collection.py::test_substitute_parameters 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_substitute_parameters 
dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_bfill_all_nan_partition 
[gw1] [ 56%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_ffill_bfill_all_nan_partition 
dask/dataframe/dask_expr/tests/test_collection.py::test_copy 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_copy 
dask/dataframe/dask_expr/tests/test_collection.py::test_partitions 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_partitions 
dask/dataframe/dask_expr/tests/test_collection.py::test_get_partition 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_get_partition 
dask/dataframe/dask_expr/tests/test_collection.py::test_column_getattr 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_to_parquet_with_get[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_select_partitioned_column[pyarrow] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_column_getattr 
dask/dataframe/dask_expr/tests/test_collection.py::test_serialization 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_select_partitioned_column[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_arrow_partitioning 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_serialization 
dask/dataframe/dask_expr/tests/test_collection.py::test_size_optimized 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_size_optimized 
dask/dataframe/dask_expr/tests/test_collection.py::test_series_iter 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_arrow_partitioning 
dask/dataframe/io/tests/test_parquet.py::test_informative_error_messages 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_informative_error_messages 
dask/dataframe/io/tests/test_parquet.py::test_append_cat_fp[pyarrow] 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_append_cat_fp[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df0] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_series_iter 
dask/dataframe/dask_expr/tests/test_collection.py::test_dataframe_iterrows 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df0] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df1] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df1] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df2] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df2] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df3] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dataframe_iterrows 
dask/dataframe/dask_expr/tests/test_collection.py::test_dataframe_itertuples 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dataframe_itertuples 
dask/dataframe/dask_expr/tests/test_collection.py::test_array_assignment 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df3] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df4] 
[gw1] [ 56%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_array_assignment 
dask/dataframe/dask_expr/tests/test_collection.py::test_columns_assignment 
[gw0] [ 56%] FAILED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df4] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df5] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df5] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df6] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df6] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df7] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df7] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df8] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df8] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df9] 
[gw0] [ 56%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df9] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df10] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df10] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df11] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df11] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df12] 
[gw1] [ 57%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_columns_assignment 
dask/dataframe/dask_expr/tests/test_collection.py::test_parent_mixed_column_assignment 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df12] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df13] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df13] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df14] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df14] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df15] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df15] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df16] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df16] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df17] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df17] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df18] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df18] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df19] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df19] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df20] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df20] 
dask/dataframe/io/tests/test_parquet.py::test_datasets_timeseries[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_datasets_timeseries[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_pathlib_path[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_pathlib_path[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_read_glob_no_meta[pyarrow-pyarrow] 
[gw1] [ 57%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_parent_mixed_column_assignment 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_meta 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_glob_no_meta[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_read_glob_yes_meta[pyarrow-pyarrow] 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_meta 
dask/dataframe/dask_expr/tests/test_collection.py::test_setattr 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_glob_yes_meta[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_read_dir_nometa[pyarrow-pyarrow-True-True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_dir_nometa[pyarrow-pyarrow-True-True] 
dask/dataframe/io/tests/test_parquet.py::test_read_dir_nometa[pyarrow-pyarrow-True-False] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_setattr 
dask/dataframe/dask_expr/tests/test_collection.py::test_rename_projection_no_renamer 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_dir_nometa[pyarrow-pyarrow-True-False] 
dask/dataframe/io/tests/test_parquet.py::test_read_dir_nometa[pyarrow-pyarrow-False-True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_dir_nometa[pyarrow-pyarrow-False-True] 
dask/dataframe/io/tests/test_parquet.py::test_read_dir_nometa[pyarrow-pyarrow-False-False] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_rename_projection_no_renamer 
dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index_projection_drop 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_dir_nometa[pyarrow-pyarrow-False-False] 
dask/dataframe/io/tests/test_parquet.py::test_statistics_nometa[pyarrow-pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_statistics_nometa[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_timeseries_nulls_in_schema[pyarrow-infer] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index_projection_drop 
dask/dataframe/dask_expr/tests/test_collection.py::test_setitem_triggering_realign 
[gw1] [ 57%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_setitem_triggering_realign 
dask/dataframe/dask_expr/tests/test_collection.py::test_is_monotonic_dt64 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_timeseries_nulls_in_schema[pyarrow-infer] 
dask/dataframe/io/tests/test_parquet.py::test_timeseries_nulls_in_schema[pyarrow-None] 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_is_monotonic_dt64 
dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[list] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[list] 
dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[array] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[array] 
dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[Series] 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_timeseries_nulls_in_schema[pyarrow-None] 
dask/dataframe/io/tests/test_parquet.py::test_graph_size_pyarrow[pyarrow] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[Series] 
dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[Index] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_graph_size_pyarrow[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_getitem_optimization_multi[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_getitem_optimization_multi[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_split_row_groups[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_row_groups[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int[pyarrow-True-1] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int[pyarrow-True-1] 
dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int[pyarrow-True-12] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[Index] 
dask/dataframe/dask_expr/tests/test_collection.py::test_enforce_runtime_divisions 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_enforce_runtime_divisions 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int[pyarrow-True-12] 
dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int[pyarrow-False-1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_apply_infer_columns 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int[pyarrow-False-1] 
dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int[pyarrow-False-12] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int[pyarrow-False-12] 
dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int_aggregate_files[pyarrow-8] 
[gw1] [ 57%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_apply_infer_columns 
dask/dataframe/dask_expr/tests/test_collection.py::test_tree_repr[True] 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_tree_repr[True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_tree_repr[False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int_aggregate_files[pyarrow-8] 
dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int_aggregate_files[pyarrow-25] 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_tree_repr[False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_random_partitions 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_int_aggregate_files[pyarrow-25] 
dask/dataframe/io/tests/test_parquet.py::test_filter_nulls[pyarrow-True-filters0-<lambda>-13] 
[gw0] [ 57%] XFAIL dask/dataframe/io/tests/test_parquet.py::test_filter_nulls[pyarrow-True-filters0-<lambda>-13] 
dask/dataframe/io/tests/test_parquet.py::test_filter_nulls[pyarrow-True-filters1-<lambda>-2] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_filter_nulls[pyarrow-True-filters1-<lambda>-2] 
dask/dataframe/io/tests/test_parquet.py::test_filter_nulls[pyarrow-False-filters0-<lambda>-13] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_random_partitions 
dask/dataframe/dask_expr/tests/test_collection.py::test_values 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_values 
dask/dataframe/dask_expr/tests/test_collection.py::test_depth 
[gw0] [ 57%] XFAIL dask/dataframe/io/tests/test_parquet.py::test_filter_nulls[pyarrow-False-filters0-<lambda>-13] 
dask/dataframe/io/tests/test_parquet.py::test_filter_nulls[pyarrow-False-filters1-<lambda>-2] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_filter_nulls[pyarrow-False-filters1-<lambda>-2] 
dask/dataframe/io/tests/test_parquet.py::test_filter_isna[True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_filter_isna[True] 
dask/dataframe/io/tests/test_parquet.py::test_filter_isna[False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_filter_isna[False] 
dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_filter[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_row_groups_filter[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_optimize_getitem_and_nonblockwise[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_optimize_getitem_and_nonblockwise[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_optimize_and_not[pyarrow] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_depth 
dask/dataframe/dask_expr/tests/test_collection.py::test_partitions_nested 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_optimize_and_not[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_empty[pyarrow-pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_empty[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[4096-None-True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[4096-None-True] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[4096-None-False] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_partitions_nested 
dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[7-True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[4096-None-False] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[4096-a-True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[4096-a-True] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[4096-a-False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[4096-a-False] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[1MiB-None-True] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[7-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[7-False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[1MiB-None-True] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[1MiB-None-False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[1MiB-None-False] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[1MiB-a-True] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[7-False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[12-True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[1MiB-a-True] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[1MiB-a-False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_files[1MiB-a-False] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_aggregate_files[a] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[12-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[12-False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_aggregate_files[a] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_aggregate_files[b] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_aggregate_files[b] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-None-True] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[12-False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_divisions[True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-None-True] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-None-False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-None-False] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-1024-True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-1024-True] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-1024-False] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_divisions[True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_divisions[False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-1024-False] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-4096-True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-4096-True] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-4096-False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-4096-False] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-1MiB-True] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_divisions[False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_no_op 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-1MiB-True] 
dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-1MiB-False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_split_adaptive_blocksize[pyarrow-1MiB-False] 
dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-default-True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-default-True] 
dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-default-False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-default-False] 
dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-512-True] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_no_op 
dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_partition_size 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-512-True] 
dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-512-False] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_partition_size 
dask/dataframe/dask_expr/tests/test_collection.py::test_len 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-512-False] 
dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-1024-True] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_len 
dask/dataframe/dask_expr/tests/test_collection.py::test_astype_simplify 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-1024-True] 
dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-1024-False] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_astype_simplify 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates[1] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-1024-False] 
dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-1MiB-True] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-1MiB-True] 
dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-1MiB-False] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_blocksize[pyarrow-1MiB-False] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_pandas_blocksize[pyarrow-pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_pandas_blocksize[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_filter_nonpartition_columns[pyarrow-pyarrow-None] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates[1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates[True] 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_filter_nonpartition_columns[pyarrow-pyarrow-None] 
dask/dataframe/io/tests/test_parquet.py::test_filter_nonpartition_columns[pyarrow-pyarrow-True] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates[True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_split_out 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_filter_nonpartition_columns[pyarrow-pyarrow-True] 
dask/dataframe/io/tests/test_parquet.py::test_pandas_metadata_nullable_pyarrow 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_split_out 
dask/dataframe/dask_expr/tests/test_collection.py::test_walk 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_pandas_metadata_nullable_pyarrow 
dask/dataframe/io/tests/test_parquet.py::test_pandas_timestamp_overflow_pyarrow 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_pandas_timestamp_overflow_pyarrow 
dask/dataframe/io/tests/test_parquet.py::test_arrow_to_pandas[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_arrow_to_pandas[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_partitioned_column_overlap[pyarrow-write_cols0] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_partitioned_column_overlap[pyarrow-write_cols0] 
dask/dataframe/io/tests/test_parquet.py::test_partitioned_column_overlap[pyarrow-write_cols1] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_partitioned_column_overlap[pyarrow-write_cols1] 
dask/dataframe/io/tests/test_parquet.py::test_partitioned_no_pandas_metadata[pyarrow-write_cols0] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_walk 
dask/dataframe/dask_expr/tests/test_collection.py::test_find_operations 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_find_operations 
dask/dataframe/dask_expr/tests/test_collection.py::test_dropna_simplify[x] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dropna_simplify[x] 
dask/dataframe/dask_expr/tests/test_collection.py::test_dropna_simplify[subset1] 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_partitioned_no_pandas_metadata[pyarrow-write_cols0] 
dask/dataframe/io/tests/test_parquet.py::test_partitioned_no_pandas_metadata[pyarrow-write_cols1] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dropna_simplify[subset1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_dir 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dir 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-replace-args0] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-replace-args0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-isin-args1] 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_partitioned_no_pandas_metadata[pyarrow-write_cols1] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_directory_partitioning 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-isin-args1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-clip-args2] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-clip-args2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-isna-args3] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-isna-args3] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-round-args4] 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_directory_partitioning 
dask/dataframe/io/tests/test_parquet.py::test_partitioned_preserve_index[pyarrow-pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_partitioned_preserve_index[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_from_pandas_preserve_none_index[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_from_pandas_preserve_none_index[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_multi_partition_none_index_false[pyarrow] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-round-args4] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-abs-args5] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_multi_partition_none_index_false[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_from_pandas_preserve_none_rangeindex[pyarrow-pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_from_pandas_preserve_none_rangeindex[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_illegal_column_name[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_illegal_column_name[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_divisions_with_null_partition[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_divisions_with_null_partition[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_simple[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_simple[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_partitioned[pyarrow-True] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-abs-args5] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-fillna-args6] 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_partitioned[pyarrow-True] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_partitioned[pyarrow-False] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-fillna-args6] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-replace-args0] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-replace-args0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-isin-args1] 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_partitioned[pyarrow-False] 
dask/dataframe/io/tests/test_parquet.py::test_null_partition_pyarrow[None] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_null_partition_pyarrow[None] 
dask/dataframe/io/tests/test_parquet.py::test_null_partition_pyarrow[processes] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-isin-args1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-clip-args2] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-clip-args2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-isna-args3] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-isna-args3] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-round-args4] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-round-args4] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-abs-args5] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-abs-args5] 
dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-fillna-args6] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-fillna-args6] 
dask/dataframe/dask_expr/tests/test_collection.py::test_isin_as_predicate 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_isin_as_predicate 
dask/dataframe/dask_expr/tests/test_collection.py::test_sample 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_sample 
dask/dataframe/dask_expr/tests/test_collection.py::test_align 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_align 
dask/dataframe/dask_expr/tests/test_collection.py::test_align_different_partitions 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_align_different_partitions 
dask/dataframe/dask_expr/tests/test_collection.py::test_align_unknown_partitions_same_root 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_align_unknown_partitions_same_root 
dask/dataframe/dask_expr/tests/test_collection.py::test_unknown_partitions_different_root 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_unknown_partitions_different_root 
dask/dataframe/dask_expr/tests/test_collection.py::test_split_out_drop_duplicates[None] 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_split_out_drop_duplicates[None] 
dask/dataframe/dask_expr/tests/test_collection.py::test_split_out_drop_duplicates[2] 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_split_out_drop_duplicates[2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_nunique[False] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nunique[False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_nunique[True] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nunique[True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_nunique_approx 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nunique_approx 
dask/dataframe/dask_expr/tests/test_collection.py::test_memory_usage_per_partition 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_null_partition_pyarrow[processes] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_read_from_paths 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_memory_usage_per_partition 
dask/dataframe/dask_expr/tests/test_collection.py::test_assign_simplify 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_read_from_paths 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_filter_partitioned[True] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_simplify 
dask/dataframe/dask_expr/tests/test_collection.py::test_assign_simplify_new_column_not_needed 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_filter_partitioned[True] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_filter_partitioned[False] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_simplify_new_column_not_needed 
dask/dataframe/dask_expr/tests/test_collection.py::test_assign_simplify_series 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_filter_partitioned[False] 
dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_filter_on_partitioned[pyarrow] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_simplify_series 
dask/dataframe/dask_expr/tests/test_collection.py::test_assign_non_series_inputs 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_non_series_inputs 
dask/dataframe/dask_expr/tests/test_collection.py::test_assign_squash_together 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_squash_together 
dask/dataframe/dask_expr/tests/test_collection.py::test_rename_series_reduction 
[gw0] [ 57%] FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_filter_on_partitioned[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_parquet_pyarrow_write_empty_metadata 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_parquet_pyarrow_write_empty_metadata 
dask/dataframe/io/tests/test_parquet.py::test_parquet_pyarrow_write_empty_metadata_append 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_parquet_pyarrow_write_empty_metadata_append 
dask/dataframe/io/tests/test_parquet.py::test_create_metadata_file[None] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_rename_series_reduction 
dask/dataframe/dask_expr/tests/test_collection.py::test_are_co_aligned 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_create_metadata_file[None] 
dask/dataframe/io/tests/test_parquet.py::test_create_metadata_file[a] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_are_co_aligned 
dask/dataframe/dask_expr/tests/test_collection.py::test_assign_pandas_inputs 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_create_metadata_file[a] 
dask/dataframe/io/tests/test_parquet.py::test_read_write_overwrite_is_true[pyarrow] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_pandas_inputs 
dask/dataframe/dask_expr/tests/test_collection.py::test_astype_categories 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_write_overwrite_is_true[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_read_write_partition_on_overwrite_is_true[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_write_partition_on_overwrite_is_true[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_to_parquet_overwrite_adaptive_round_trip[pyarrow] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_astype_categories 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_simplify 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_to_parquet_overwrite_adaptive_round_trip[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_to_parquet_overwrite_raises[pyarrow] 
[gw0] [ 57%] PASSED dask/dataframe/io/tests/test_parquet.py::test_to_parquet_overwrite_raises[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_to_parquet_overwrite_files_from_read_parquet_in_same_call_raises[pyarrow] 
[gw1] [ 57%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_simplify 
dask/dataframe/dask_expr/tests/test_collection.py::test_op_align 
[gw1] [ 57%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_op_align 
dask/dataframe/dask_expr/tests/test_collection.py::test_can_co_align 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_can_co_align 
dask/dataframe/dask_expr/tests/test_collection.py::test_avoid_alignment 
[gw0] [ 58%] FAILED dask/dataframe/io/tests/test_parquet.py::test_to_parquet_overwrite_files_from_read_parquet_in_same_call_raises[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_to_parquet_errors_non_string_column_names[pyarrow] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_to_parquet_errors_non_string_column_names[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_dir_filter[pyarrow] 
[gw1] [ 58%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_avoid_alignment 
dask/dataframe/dask_expr/tests/test_collection.py::test_mixed_array_op 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_mixed_array_op 
dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[add] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[add] 
dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[sub] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[sub] 
dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[mul] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[mul] 
dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[truediv] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[truediv] 
dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[pow] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[pow] 
dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[mod] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_array_series_comp[mod] 
dask/dataframe/dask_expr/tests/test_collection.py::test_len_shuffle_repartition 
[gw0] [ 58%] FAILED dask/dataframe/io/tests/test_parquet.py::test_dir_filter[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_decimal_dtype 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_decimal_dtype 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_date_dtype 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_date_dtype 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_rename_columns[pyarrow] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_rename_columns[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_custom_metadata[pyarrow] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_custom_metadata[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_ignore_metadata_file[pyarrow-True] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_len_shuffle_repartition 
dask/dataframe/dask_expr/tests/test_collection.py::test_columns_setter 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_ignore_metadata_file[pyarrow-True] 
dask/dataframe/io/tests/test_parquet.py::test_ignore_metadata_file[pyarrow-False] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_ignore_metadata_file[pyarrow-False] 
dask/dataframe/io/tests/test_parquet.py::test_ignore_metadata_file[pyarrow-None] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_ignore_metadata_file[pyarrow-None] 
dask/dataframe/io/tests/test_parquet.py::test_metadata_task_size[pyarrow-2-True] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_columns_setter 
dask/dataframe/dask_expr/tests/test_collection.py::test_contains 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_contains 
dask/dataframe/dask_expr/tests/test_collection.py::test_filter_pushdown_unavailable 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_metadata_task_size[pyarrow-2-True] 
dask/dataframe/io/tests/test_parquet.py::test_metadata_task_size[pyarrow-2-False] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_filter_pushdown_unavailable 
dask/dataframe/dask_expr/tests/test_collection.py::test_filter_pushdown 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_metadata_task_size[pyarrow-2-False] 
dask/dataframe/io/tests/test_parquet.py::test_metadata_task_size[pyarrow-0-True] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_filter_pushdown 
dask/dataframe/dask_expr/tests/test_collection.py::test_shape 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_metadata_task_size[pyarrow-0-True] 
dask/dataframe/io/tests/test_parquet.py::test_metadata_task_size[pyarrow-0-False] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shape 
dask/dataframe/dask_expr/tests/test_collection.py::test_size 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_size 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_groupby 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_metadata_task_size[pyarrow-0-False] 
dask/dataframe/io/tests/test_parquet.py::test_extra_file[pyarrow-b] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_extra_file[pyarrow-b] 
dask/dataframe/io/tests/test_parquet.py::test_extra_file[pyarrow-None] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_groupby 
dask/dataframe/dask_expr/tests/test_collection.py::test_expression_bool_raises 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_expression_bool_raises 
dask/dataframe/dask_expr/tests/test_collection.py::test_expr_is_scalar 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_expr_is_scalar 
dask/dataframe/dask_expr/tests/test_collection.py::test_replace_filtered_combine_similar 
[gw0] [ 58%] FAILED dask/dataframe/io/tests/test_parquet.py::test_extra_file[pyarrow-None] 
dask/dataframe/io/tests/test_parquet.py::test_unsupported_extension_file[pyarrow] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_unsupported_extension_file[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_unsupported_extension_dir[pyarrow] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_unsupported_extension_dir[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_custom_filename[pyarrow] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_custom_filename[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_custom_filename_works_with_pyarrow_when_append_is_true 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_custom_filename_works_with_pyarrow_when_append_is_true 
dask/dataframe/io/tests/test_parquet.py::test_throws_error_if_custom_filename_is_invalid[pyarrow] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_throws_error_if_custom_filename_is_invalid[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_custom_filename_with_partition[pyarrow] 
[gw1] [ 58%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_replace_filtered_combine_similar 
dask/dataframe/dask_expr/tests/test_collection.py::test_quantile_frame 
[gw0] [ 58%] FAILED dask/dataframe/io/tests/test_parquet.py::test_custom_filename_with_partition[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_roundtrip_partitioned_pyarrow_dataset[pyarrow] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_quantile_frame 
dask/dataframe/dask_expr/tests/test_collection.py::test_combine 
[gw0] [ 58%] FAILED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_partitioned_pyarrow_dataset[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_in_predicate_can_use_iterables[pyarrow-set] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_in_predicate_can_use_iterables[pyarrow-set] 
dask/dataframe/io/tests/test_parquet.py::test_in_predicate_can_use_iterables[pyarrow-list] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_in_predicate_can_use_iterables[pyarrow-list] 
dask/dataframe/io/tests/test_parquet.py::test_in_predicate_can_use_iterables[pyarrow-tuple] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_in_predicate_can_use_iterables[pyarrow-tuple] 
dask/dataframe/io/tests/test_parquet.py::test_not_in_predicate[pyarrow] 
[gw1] [ 58%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_combine 
dask/dataframe/dask_expr/tests/test_collection.py::test_quantile 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_not_in_predicate[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_in_predicate_requires_an_iterable[pyarrow-one-item-single-nest] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_in_predicate_requires_an_iterable[pyarrow-one-item-single-nest] 
dask/dataframe/io/tests/test_parquet.py::test_in_predicate_requires_an_iterable[pyarrow-one-item-double-nest] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_in_predicate_requires_an_iterable[pyarrow-one-item-double-nest] 
dask/dataframe/io/tests/test_parquet.py::test_in_predicate_requires_an_iterable[pyarrow-two-item-double-nest] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_in_predicate_requires_an_iterable[pyarrow-two-item-double-nest] 
dask/dataframe/io/tests/test_parquet.py::test_in_predicate_requires_an_iterable[pyarrow-two-item-two-nest] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_in_predicate_requires_an_iterable[pyarrow-two-item-two-nest] 
dask/dataframe/io/tests/test_parquet.py::test_gpu_write_parquet_simple 
[gw0] [ 58%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_gpu_write_parquet_simple 
dask/dataframe/io/tests/test_parquet.py::test_fsspec_to_parquet_filesystem_option 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_fsspec_to_parquet_filesystem_option 
dask/dataframe/io/tests/test_parquet.py::test_select_filtered_column[pyarrow] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_select_filtered_column[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_select_filtered_column_no_stats[pyarrow] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_quantile 
dask/dataframe/dask_expr/tests/test_collection.py::test_align_axis[inner] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_select_filtered_column_no_stats[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_read_parquet_convert_string[pyarrow-True] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_parquet_convert_string[pyarrow-True] 
dask/dataframe/io/tests/test_parquet.py::test_read_parquet_convert_string[pyarrow-False] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_parquet_convert_string[pyarrow-False] 
dask/dataframe/io/tests/test_parquet.py::test_read_parquet_convert_string_nullable_mapper[pyarrow] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_parquet_convert_string_nullable_mapper[pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_dtype_backend[pyarrow-numpy_nullable] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_dtype_backend[pyarrow-numpy_nullable] 
dask/dataframe/io/tests/test_parquet.py::test_dtype_backend[pyarrow-pyarrow] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_dtype_backend[pyarrow-pyarrow] 
dask/dataframe/io/tests/test_parquet.py::test_read_parquet_preserve_categorical_column_dtype 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_align_axis[inner] 
dask/dataframe/dask_expr/tests/test_collection.py::test_align_axis[outer] 
[gw0] [ 58%] FAILED dask/dataframe/io/tests/test_parquet.py::test_read_parquet_preserve_categorical_column_dtype 
dask/dataframe/io/tests/test_parquet.py::test_dtype_backend_categoricals 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_dtype_backend_categoricals 
dask/dataframe/io/tests/test_parquet.py::test_non_categorical_partitioning_pyarrow[None] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_non_categorical_partitioning_pyarrow[None] 
dask/dataframe/io/tests/test_parquet.py::test_non_categorical_partitioning_pyarrow[filters1] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_non_categorical_partitioning_pyarrow[filters1] 
dask/dataframe/io/tests/test_parquet.py::test_read_parquet_lists_not_converting 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_read_parquet_lists_not_converting 
dask/dataframe/io/tests/test_parquet.py::test_parquet_string_roundtrip 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_parquet.py::test_parquet_string_roundtrip 
dask/dataframe/io/tests/test_parquet.py::test_parquet_botocore_exception 
[gw0] [ 58%] SKIPPED dask/dataframe/io/tests/test_parquet.py::test_parquet_botocore_exception 
dask/dataframe/io/tests/test_sql.py::test_empty 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_empty 
dask/dataframe/io/tests/test_sql.py::test_single_column[True] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_align_axis[outer] 
dask/dataframe/dask_expr/tests/test_collection.py::test_align_axis[left] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_single_column[True] 
dask/dataframe/io/tests/test_sql.py::test_single_column[False] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_single_column[False] 
dask/dataframe/io/tests/test_sql.py::test_passing_engine_as_uri_raises_helpful_error 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_passing_engine_as_uri_raises_helpful_error 
dask/dataframe/io/tests/test_sql.py::test_empty_other_schema 
[gw0] [ 58%] SKIPPED dask/dataframe/io/tests/test_sql.py::test_empty_other_schema 
dask/dataframe/io/tests/test_sql.py::test_needs_rational 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_align_axis[left] 
dask/dataframe/dask_expr/tests/test_collection.py::test_align_axis[right] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_needs_rational 
dask/dataframe/io/tests/test_sql.py::test_simple 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_align_axis[right] 
dask/dataframe/dask_expr/tests/test_collection.py::test_quantile_datetime_numeric_only_false 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_simple 
dask/dataframe/io/tests/test_sql.py::test_npartitions 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_quantile_datetime_numeric_only_false 
dask/dataframe/dask_expr/tests/test_collection.py::test_dtype 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_npartitions 
dask/dataframe/io/tests/test_sql.py::test_divisions 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dtype 
dask/dataframe/dask_expr/tests/test_collection.py::test_isnull 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_divisions 
dask/dataframe/io/tests/test_sql.py::test_division_or_partition 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_isnull 
dask/dataframe/dask_expr/tests/test_collection.py::test_scalar_to_series 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_scalar_to_series 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_datetime[B-False] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_division_or_partition 
dask/dataframe/io/tests/test_sql.py::test_meta 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_meta 
dask/dataframe/io/tests/test_sql.py::test_meta_no_head_rows 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_datetime[B-False] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_datetime[D-True] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_meta_no_head_rows 
dask/dataframe/io/tests/test_sql.py::test_no_meta_no_head_rows 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_no_meta_no_head_rows 
dask/dataframe/io/tests/test_sql.py::test_limits 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_limits 
dask/dataframe/io/tests/test_sql.py::test_datetimes 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_datetimes 
dask/dataframe/io/tests/test_sql.py::test_extra_connection_engine_keywords 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_datetime[D-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_datetime[h-True] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_extra_connection_engine_keywords 
dask/dataframe/io/tests/test_sql.py::test_query 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_query 
dask/dataframe/io/tests/test_sql.py::test_query_index_from_query 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_datetime[h-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_isin_head 
[gw0] [ 58%] FAILED dask/dataframe/io/tests/test_sql.py::test_query_index_from_query 
dask/dataframe/io/tests/test_sql.py::test_query_with_meta 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_query_with_meta 
dask/dataframe/io/tests/test_sql.py::test_no_character_index_without_divisions 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_no_character_index_without_divisions 
dask/dataframe/io/tests/test_sql.py::test_read_sql 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_isin_head 
dask/dataframe/dask_expr/tests/test_collection.py::test_head_npartitions_divisions 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_read_sql 
dask/dataframe/io/tests/test_sql.py::test_to_sql[False-1] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head_npartitions_divisions 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_period_index[D-True] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_period_index[D-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_period_index[h-True] 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_to_sql[False-1] 
dask/dataframe/io/tests/test_sql.py::test_to_sql[False-2] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_period_index[h-True] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_TimedeltaIndex[min] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_TimedeltaIndex[min] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_TimedeltaIndex[D] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_TimedeltaIndex[D] 
dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_TimedeltaIndex[h] 
[gw0] [ 58%] FAILED dask/dataframe/io/tests/test_sql.py::test_to_sql[False-2] 
dask/dataframe/io/tests/test_sql.py::test_to_sql[True-1] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_TimedeltaIndex[h] 
dask/dataframe/dask_expr/tests/test_collection.py::test_iter 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_iter 
dask/dataframe/dask_expr/tests/test_collection.py::test_items 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_to_sql[True-1] 
dask/dataframe/io/tests/test_sql.py::test_to_sql[True-2] 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_items 
dask/dataframe/dask_expr/tests/test_collection.py::test_isin_strings 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_isin_strings 
dask/dataframe/dask_expr/tests/test_collection.py::test_predicate_pushdown_ndim_change 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_predicate_pushdown_ndim_change 
dask/dataframe/dask_expr/tests/test_collection.py::test_axes 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_axes 
dask/dataframe/dask_expr/tests/test_collection.py::test_filter_optimize_condition 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_filter_optimize_condition 
dask/dataframe/dask_expr/tests/test_collection.py::test_scalar_repr 
[gw0] [ 58%] FAILED dask/dataframe/io/tests/test_sql.py::test_to_sql[True-2] 
dask/dataframe/io/tests/test_sql.py::test_to_sql_kwargs 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_to_sql_kwargs 
dask/dataframe/io/tests/test_sql.py::test_to_sql_engine_kwargs 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_scalar_repr 
dask/dataframe/dask_expr/tests/test_collection.py::test_array_conversion 
[gw0] [ 58%] PASSED dask/dataframe/io/tests/test_sql.py::test_to_sql_engine_kwargs 
dask/dataframe/tests/test_accessors.py::test_register[Series-register_series_accessor] 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_accessors.py::test_register[Series-register_series_accessor] 
dask/dataframe/tests/test_accessors.py::test_register[DataFrame-register_dataframe_accessor] 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_accessors.py::test_register[DataFrame-register_dataframe_accessor] 
dask/dataframe/tests/test_accessors.py::test_register[Index-register_index_accessor] 
[gw0] [ 58%] SKIPPED dask/dataframe/tests/test_accessors.py::test_register[Index-register_index_accessor] 
dask/dataframe/tests/test_accessors.py::test_accessor_works 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_accessors.py::test_accessor_works 
dask/dataframe/tests/test_accessors.py::test_dt_accessor 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_array_conversion 
dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index_filter_pushdown 
[gw0] [ 58%] ERROR dask/dataframe/tests/test_accessors.py::test_dt_accessor 
dask/dataframe/tests/test_accessors.py::test_dt_accessor_not_available 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index_filter_pushdown 
dask/dataframe/dask_expr/tests/test_collection.py::test_astype_filter_pushdown 
[gw0] [ 58%] ERROR dask/dataframe/tests/test_accessors.py::test_dt_accessor_not_available 
dask/dataframe/tests/test_accessors.py::test_str_accessor 
[gw0] [ 58%] ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor 
dask/dataframe/tests/test_accessors.py::test_str_accessor_not_available 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_astype_filter_pushdown 
dask/dataframe/dask_expr/tests/test_collection.py::test_warn_annotations 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_warn_annotations 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_columns_with_common_prefix 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_drop_columns_with_common_prefix 
dask/dataframe/dask_expr/tests/test_collection.py::test_to_datetime_repr 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_to_datetime_repr 
dask/dataframe/dask_expr/tests/test_collection.py::test_drop_set 
[gw0] [ 58%] ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_not_available 
dask/dataframe/tests/test_accessors.py::test_str_accessor_getitem 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_set 
dask/dataframe/dask_expr/tests/test_collection.py::test_shape_integer 
[gw0] [ 58%] ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_getitem 
dask/dataframe/tests/test_accessors.py::test_str_accessor_extractall 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shape_integer 
dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs0] 
[gw0] [ 58%] ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_extractall 
dask/dataframe/tests/test_accessors.py::test_str_accessor_removeprefix_removesuffix[removeprefix] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs0] 
dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs1] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs1] 
dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs2] 
[gw0] [ 58%] ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_removeprefix_removesuffix[removeprefix] 
dask/dataframe/tests/test_accessors.py::test_str_accessor_removeprefix_removesuffix[removesuffix] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs2] 
dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs3] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs3] 
dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs4] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs4] 
dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs5] 
[gw0] [ 58%] ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_removeprefix_removesuffix[removesuffix] 
dask/dataframe/tests/test_accessors.py::test_str_accessor_cat 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs5] 
dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs6] 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_melt[kwargs6] 
dask/dataframe/dask_expr/tests/test_collection.py::test_assign_projection_mix 
[gw0] [ 58%] ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_cat 
dask/dataframe/tests/test_accessors.py::test_str_accessor_cat_none 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_accessors.py::test_str_accessor_cat_none 
dask/dataframe/tests/test_accessors.py::test_str_accessor_split_noexpand[split] 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_accessors.py::test_str_accessor_split_noexpand[split] 
dask/dataframe/tests/test_accessors.py::test_str_accessor_split_noexpand[rsplit] 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_accessors.py::test_str_accessor_split_noexpand[rsplit] 
dask/dataframe/tests/test_accessors.py::test_str_accessor_split_expand[split] 
[gw1] [ 58%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_assign_projection_mix 
dask/dataframe/dask_expr/tests/test_collection.py::test_assign_overwriting_column 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_accessors.py::test_str_accessor_split_expand[split] 
dask/dataframe/tests/test_accessors.py::test_str_accessor_split_expand[rsplit] 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_accessors.py::test_str_accessor_split_expand[rsplit] 
dask/dataframe/tests/test_accessors.py::test_str_accessor_split_expand_more_columns 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_overwriting_column 
dask/dataframe/dask_expr/tests/test_collection.py::test_dropna_merge 
[gw0] [ 58%] XFAIL dask/dataframe/tests/test_accessors.py::test_str_accessor_split_expand_more_columns 
dask/dataframe/tests/test_accessors.py::test_str_split_no_warning[range_index] 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_accessors.py::test_str_split_no_warning[range_index] 
dask/dataframe/tests/test_accessors.py::test_str_split_no_warning[other index] 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_accessors.py::test_str_split_no_warning[other index] 
dask/dataframe/tests/test_accessors.py::test_string_nullable_types 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dropna_merge 
dask/dataframe/dask_expr/tests/test_collection.py::test_empty_from_pandas_projection 
[gw0] [ 58%] ERROR dask/dataframe/tests/test_accessors.py::test_string_nullable_types 
dask/dataframe/tests/test_api.py::test_api 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_api.py::test_api 
dask/dataframe/tests/test_arithmetics_reduction.py::test_deterministic_arithmetic_names 
[gw0] [ 58%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_deterministic_arithmetic_names 
dask/dataframe/tests/test_arithmetics_reduction.py::test_arithmetics_different_index 
[gw0] [ 58%] SKIPPED dask/dataframe/tests/test_arithmetics_reduction.py::test_arithmetics_different_index 
dask/dataframe/tests/test_arithmetics_reduction.py::test_frame_series_arithmetic_methods 
[gw1] [ 58%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_empty_from_pandas_projection 
dask/dataframe/dask_expr/tests/test_collection.py::test_binop_scalar_left 
[gw1] [ 58%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_binop_scalar_left 
dask/dataframe/dask_expr/tests/test_collection.py::test_to_backend_simplify 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_to_backend_simplify 
dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_triggering_unnecessary_alignment 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_triggering_unnecessary_alignment 
dask/dataframe/dask_expr/tests/test_collection.py::test_projection_on_series 
[gw1] [ 58%] PASSED dask/dataframe/dask_expr/tests/test_collection.py::test_projection_on_series 
dask/dataframe/dask_expr/tests/test_collection.py::test_align_known_divisions_in_assign 
[gw1] [ 58%] FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_align_known_divisions_in_assign 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_str 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_str 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_pdf 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_pdf 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_divisions 
[gw1] [ 58%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_divisions 
dask/dataframe/dask_expr/tests/test_concat.py::test_invalid_joins[right] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_invalid_joins[right] 
dask/dataframe/dask_expr/tests/test_concat.py::test_invalid_joins[left] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_invalid_joins[left] 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_invalid 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_invalid 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_one_object 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_one_object 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_one_no_columns 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_one_no_columns 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_simplify 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_simplify 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_simplify_projection_not_added 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_simplify_projection_not_added 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_axis_one_co_aligned 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_axis_one_co_aligned 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_axis_one_all_divisions_unknown 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_axis_one_all_divisions_unknown 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_axis_one_drop_dfs_not_selected 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_axis_one_drop_dfs_not_selected 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_ignore_order 
[gw1] [ 59%] FAILED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_ignore_order 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_index 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_index 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_one_series 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_one_series 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_dataframe_empty 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_dataframe_empty 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_after_merge 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_after_merge 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat4_interleave_partitions 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat4_interleave_partitions 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat5 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat5 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_series 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_series 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_series_and_projection 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_series_and_projection 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_single_partition_known_divisions[inner-1] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_single_partition_known_divisions[inner-1] 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_single_partition_known_divisions[inner-2] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_single_partition_known_divisions[inner-2] 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_single_partition_known_divisions[outer-1] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_single_partition_known_divisions[outer-1] 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_single_partition_known_divisions[outer-2] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_single_partition_known_divisions[outer-2] 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_mixed_dtype_columns 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_mixed_dtype_columns 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_optimize_project[True-0] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_optimize_project[True-0] 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_optimize_project[True-1] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_optimize_project[True-1] 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_optimize_project[False-0] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_optimize_project[False-0] 
dask/dataframe/dask_expr/tests/test_concat.py::test_concat_optimize_project[False-1] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_optimize_project[False-1] 
dask/dataframe/dask_expr/tests/test_core.py::test_endless_simplify 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_core.py::test_endless_simplify 
dask/dataframe/dask_expr/tests/test_core.py::test_expr_nondeterministic_token_pickle_roundtrip 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_core.py::test_expr_nondeterministic_token_pickle_roundtrip 
dask/dataframe/dask_expr/tests/test_core.py::test_combine_multiple_dataframes 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_core.py::test_combine_multiple_dataframes 
dask/dataframe/dask_expr/tests/test_cumulative.py::test_cumulative_empty_partitions 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_cumulative.py::test_cumulative_empty_partitions 
dask/dataframe/dask_expr/tests/test_datasets.py::test_timeseries 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_timeseries 
dask/dataframe/dask_expr/tests/test_datasets.py::test_optimization 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_optimization 
dask/dataframe/dask_expr/tests/test_datasets.py::test_arrow_string_option 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_frame_series_arithmetic_methods 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions[False] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_arrow_string_option 
dask/dataframe/dask_expr/tests/test_datasets.py::test_column_projection_deterministic 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_column_projection_deterministic 
dask/dataframe/dask_expr/tests/test_datasets.py::test_timeseries_culling 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_timeseries_culling 
dask/dataframe/dask_expr/tests/test_datasets.py::test_persist 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_persist 
dask/dataframe/dask_expr/tests/test_datasets.py::test_lengths 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_lengths 
dask/dataframe/dask_expr/tests/test_datasets.py::test_timeseries_empty_projection 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_timeseries_empty_projection 
dask/dataframe/dask_expr/tests/test_datasets.py::test_combine_similar 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_combine_similar 
dask/dataframe/dask_expr/tests/test_datasets.py::test_timeseries_deterministic_head[42] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_timeseries_deterministic_head[42] 
dask/dataframe/dask_expr/tests/test_datasets.py::test_timeseries_deterministic_head[None] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_timeseries_deterministic_head[None] 
dask/dataframe/dask_expr/tests/test_datasets.py::test_dataset_head 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datasets.py::test_dataset_head 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[ceil-args0] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[ceil-args0] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[day_name-args1] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[day_name-args1] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[floor-args2] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[floor-args2] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[isocalendar-args3] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[isocalendar-args3] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[month_name-args4] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[month_name-args4] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[normalize-args5] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[normalize-args5] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[round-args6] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[round-args6] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[strftime-args7] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[strftime-args7] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[to_period-args8] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_methods[to_period-args8] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[date] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[date] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[day] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[day] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[day_of_week] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[day_of_week] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[day_of_year] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[day_of_year] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[dayofweek] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[dayofweek] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[dayofyear] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[dayofyear] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[days_in_month] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[days_in_month] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[daysinmonth] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[daysinmonth] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[hour] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[hour] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_leap_year] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_leap_year] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_month_end] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_month_end] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_month_start] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_month_start] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_quarter_end] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_quarter_end] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_quarter_start] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions[False] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions[2] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_quarter_start] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_year_end] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_year_end] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_year_start] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[is_year_start] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[microsecond] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[microsecond] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[minute] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[minute] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[month] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[month] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[nanosecond] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[nanosecond] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[quarter] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[quarter] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[second] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[second] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[time] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[time] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[timetz] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[timetz] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[weekday] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[weekday] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[year] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_datetime_accessor_properties[year] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_timedelta_accessor_properties[components] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_timedelta_accessor_properties[components] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_timedelta_accessor_properties[days] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_timedelta_accessor_properties[days] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_timedelta_accessor_properties[microseconds] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_timedelta_accessor_properties[microseconds] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_timedelta_accessor_properties[nanoseconds] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_timedelta_accessor_properties[nanoseconds] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_timedelta_accessor_properties[seconds] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_timedelta_accessor_properties[seconds] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_period_accessor_properties[end_time] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_period_accessor_properties[end_time] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_period_accessor_properties[start_time] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_period_accessor_properties[start_time] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_period_accessor_properties[qyear] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_period_accessor_properties[qyear] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_period_accessor_properties[week] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_period_accessor_properties[week] 
dask/dataframe/dask_expr/tests/test_datetime.py::test_period_accessor_properties[weekofyear] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_datetime.py::test_period_accessor_properties[weekofyear] 
dask/dataframe/dask_expr/tests/test_describe.py::test_describe_series 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_describe.py::test_describe_series 
dask/dataframe/dask_expr/tests/test_describe.py::test_describe_df 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_describe.py::test_describe_df 
dask/dataframe/dask_expr/tests/test_dummies.py::test_get_dummies[data0] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_dummies.py::test_get_dummies[data0] 
dask/dataframe/dask_expr/tests/test_dummies.py::test_get_dummies[data1] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_dummies.py::test_get_dummies[data1] 
dask/dataframe/dask_expr/tests/test_dummies.py::test_get_dummies[data2] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_dummies.py::test_get_dummies[data2] 
dask/dataframe/dask_expr/tests/test_format.py::test_to_string 
[gw1] [ 59%] SKIPPED dask/dataframe/dask_expr/tests/test_format.py::test_to_string 
dask/dataframe/dask_expr/tests/test_format.py::test_series_format 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_format.py::test_series_format 
dask/dataframe/dask_expr/tests/test_format.py::test_series_repr 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_format.py::test_series_repr 
dask/dataframe/dask_expr/tests/test_format.py::test_df_repr 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_format.py::test_df_repr 
dask/dataframe/dask_expr/tests/test_format.py::test_df_to_html 
[gw1] [ 59%] SKIPPED dask/dataframe/dask_expr/tests/test_format.py::test_df_to_html 
dask/dataframe/dask_expr/tests/test_fusion.py::test_simple 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_simple 
dask/dataframe/dask_expr/tests/test_fusion.py::test_with_non_fusable_on_top 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_with_non_fusable_on_top 
dask/dataframe/dask_expr/tests/test_fusion.py::test_optimize_fusion_many 
[gw1] [ 59%] FAILED dask/dataframe/dask_expr/tests/test_fusion.py::test_optimize_fusion_many 
dask/dataframe/dask_expr/tests/test_fusion.py::test_optimize_fusion_repeat 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_optimize_fusion_repeat 
dask/dataframe/dask_expr/tests/test_fusion.py::test_optimize_fusion_broadcast 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_optimize_fusion_broadcast 
dask/dataframe/dask_expr/tests/test_fusion.py::test_persist_with_fusion 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_persist_with_fusion 
dask/dataframe/dask_expr/tests/test_fusion.py::test_fuse_broadcast_deps 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_fusion.py::test_fuse_broadcast_deps 
dask/dataframe/dask_expr/tests/test_fusion.py::test_name 
[gw1] [ 59%] ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_name 
dask/dataframe/dask_expr/tests/test_fusion.py::test_fusion_executes_only_once 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_fusion.py::test_fusion_executes_only_once 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_unsupported_by 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_unsupported_by 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-sum-None] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-sum-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-sum-5] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-sum-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-mean-None] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-mean-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-mean-5] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions[2] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_timedelta[False] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_timedelta[False] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_timedelta[2] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_timedelta[2] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[sum-0] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[sum-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[sum-1] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[sum-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[prod-0] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[prod-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[prod-1] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[prod-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[product-0] 
[gw0] [ 59%] SKIPPED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[product-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[product-1] 
[gw0] [ 59%] SKIPPED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[product-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[min-0] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[min-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[min-1] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[min-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[max-0] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[max-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[max-1] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[max-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[mean-0] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[mean-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[mean-1] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[mean-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[var-0] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[var-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[var-1] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[var-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[std-0] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[std-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[std-1] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[std-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[all-0] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[all-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[all-1] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-mean-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-min-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[all-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[any-0] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[any-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[any-1] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_numpy_dispatch[any-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_allany[False] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-min-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-min-5] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-min-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-max-None] 
[gw0] [ 59%] XFAIL dask/dataframe/tests/test_arithmetics_reduction.py::test_allany[False] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_allany[2] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-max-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-max-5] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-max-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-prod-None] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-prod-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-prod-5] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-prod-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-first-None] 
[gw0] [ 59%] XFAIL dask/dataframe/tests/test_arithmetics_reduction.py::test_allany[2] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_deterministic_reduction_names[False] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_deterministic_reduction_names[False] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_deterministic_reduction_names[2] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_deterministic_reduction_names[2] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reduction_series_invalid_axis 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reduction_series_invalid_axis 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_non_numeric_dtypes 
[gw0] [ 59%] XFAIL dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_non_numeric_dtypes 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame[False] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-first-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-first-5] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-first-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-last-None] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-last-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-last-5] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-last-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-var-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame[False] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame[2] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-var-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-var-5] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-var-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-std-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame[2] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-sum-None] 
[gw0] [ 59%] XFAIL dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-sum-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-prod-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-prod-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-product-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-product-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-mean-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-mean-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-std-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-std-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-std-kwargs5] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-std-kwargs5] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-std-kwargs6] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-std-kwargs6] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-std-kwargs7] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-std-kwargs7] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-min-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-min-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-max-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-max-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-count-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-count-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-sem-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-sem-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-sem-kwargs12] 
[gw1] [ 59%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-std-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-std-5] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-sem-kwargs12] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-sem-kwargs13] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-sem-kwargs13] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-sem-kwargs14] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-sem-kwargs14] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-var-None] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-var-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-var-kwargs16] 
[gw0] [ 59%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-var-kwargs16] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-var-kwargs17] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-var-kwargs17] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-var-kwargs18] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[None-var-kwargs18] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-sum-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-sum-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-prod-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-prod-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-product-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-product-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-mean-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-mean-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-std-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-std-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-std-kwargs5] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-std-kwargs5] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-std-kwargs6] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-std-kwargs6] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-std-kwargs7] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-std-kwargs7] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-min-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-min-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-max-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-max-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-count-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-count-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-sem-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-sem-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-sem-kwargs12] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-std-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-idxmin-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-sem-kwargs12] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-sem-kwargs13] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-sem-kwargs13] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-sem-kwargs14] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-sem-kwargs14] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-var-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-var-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-var-kwargs16] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-idxmin-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-idxmin-5] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-var-kwargs16] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-var-kwargs17] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-var-kwargs17] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-var-kwargs18] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[True-var-kwargs18] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-sum-None] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[True-idxmin-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-sum-None] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-sum-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-sum-5] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-sum-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-mean-None] 
[gw0] [ 60%] XFAIL dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-sum-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-prod-None] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-prod-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-product-None] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-product-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-mean-None] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-mean-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-std-None] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-std-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-std-kwargs5] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-std-kwargs5] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-std-kwargs6] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-std-kwargs6] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-std-kwargs7] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-std-kwargs7] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-min-None] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-min-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-max-None] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-max-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-count-None] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-mean-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-mean-5] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-count-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-sem-None] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-sem-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-sem-kwargs12] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-sem-kwargs12] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-sem-kwargs13] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-sem-kwargs13] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-sem-kwargs14] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-sem-kwargs14] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-var-None] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-var-None] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-var-kwargs16] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-var-kwargs16] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-var-kwargs17] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-var-kwargs17] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-var-kwargs18] 
[gw0] [ 60%] XPASS dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes[False-var-kwargs18] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_count_numeric_only_axis_one 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-mean-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-min-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_count_numeric_only_axis_one 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[sum] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[sum] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[prod] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-min-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-min-5] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[prod] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[product] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[product] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[min] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-min-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-max-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[min] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[max] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-max-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-max-5] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[max] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[count] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-max-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-prod-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[count] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[std] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-prod-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-prod-5] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[std] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[var] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[var] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[quantile] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-prod-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-first-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only_supported[quantile] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only[mean] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-first-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-first-5] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-first-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-last-None] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only[mean] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only[sem] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-last-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-last-5] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_dtypes_numeric_only[sem] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_skew_kurt_numeric_only_false[skew] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_skew_kurt_numeric_only_false[skew] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_skew_kurt_numeric_only_false[kurtosis] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_skew_kurt_numeric_only_false[kurtosis] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_nan[False] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-last-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-var-None] 
[gw1] [ 60%] XFAIL dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-var-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-var-5] 
[gw1] [ 60%] XFAIL dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-var-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-std-None] 
[gw1] [ 60%] XFAIL dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-std-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-std-5] 
[gw1] [ 60%] XFAIL dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-std-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-idxmin-None] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-idxmin-None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-idxmin-5] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_nan[False] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_nan[2] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric[False-idxmin-5] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_reduction_optimize 
[gw1] [ 60%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_reduction_optimize 
dask/dataframe/dask_expr/tests/test_groupby.py::test_std_columns_int 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_std_columns_int 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_no_numeric_only[count] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_no_numeric_only[count] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_no_numeric_only[value_counts] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_frame_nan[2] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[lt] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[lt] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[gt] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[gt] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[le] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[le] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[ge] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_no_numeric_only[value_counts] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_no_numeric_only[size] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[ge] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[ne] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[ne] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[eq] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_comparison_nan[eq] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_sum_intna 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_sum_intna 
dask/dataframe/tests/test_arithmetics_reduction.py::test_divmod 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_divmod 
dask/dataframe/tests/test_arithmetics_reduction.py::test_moment 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_moment 
dask/dataframe/tests/test_arithmetics_reduction.py::test_empty_df_reductions[sum] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_no_numeric_only[size] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_no_numeric_only[head] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_empty_df_reductions[sum] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_empty_df_reductions[count] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_empty_df_reductions[count] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_empty_df_reductions[mean] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_empty_df_reductions[mean] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_empty_df_reductions[var] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_empty_df_reductions[var] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_empty_df_reductions[sem] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_no_numeric_only[head] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_no_numeric_only[tail] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_empty_df_reductions[sem] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[0-sum] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[0-sum] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[0-prod] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[0-prod] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[0-product] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[0-product] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[9-sum] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[9-sum] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[9-prod] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[9-prod] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[9-product] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_series_agg_with_min_count[9-product] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[True-0] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_no_numeric_only[tail] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_value_counts_split_out 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[True-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[True-1] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[True-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[False-0] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_value_counts_split_out 
dask/dataframe/dask_expr/tests/test_groupby.py::test_unique 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_unique 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_mean_slice 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[False-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[False-1] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_mean_slice 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_grouper_single 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_grouper_single 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_agg_reduces 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_slice_agg_reduces 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_nunique 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[False-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[None-0] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_nunique 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_series 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_series 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_group_keys[True] 
[gw1] [ 60%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_group_keys[True] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_group_keys[False] 
[gw1] [ 60%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_group_keys[False] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_group_keys[None] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[None-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[None-1] 
[gw1] [ 60%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_group_keys[None] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_dataframe_aggregations_multilevel 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_dataframe_aggregations_multilevel 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[spec0] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[spec0] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[spec1] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[spec1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[spec2] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[None-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[True-False-0] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[True-False-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[True-False-1] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[spec2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[spec3] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[True-False-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[True-True-0] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[True-True-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[True-True-1] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[True-True-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-False-0] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[spec3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[spec4] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[spec4] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[sum] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[sum] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[median] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-False-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-False-1] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg[median] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_cov[cov-True] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_cov[cov-True] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_cov[cov-False] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_cov[cov-False] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_cov[corr-True] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-False-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-True-0] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_cov[corr-True] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_cov[corr-False] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_cov[corr-False] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_agg[sum] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_agg[sum] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_agg[spec1] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_agg[spec1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_agg[spec2] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-True-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-True-1] 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_agg[spec2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_getitem_agg 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_getitem_agg 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_column_projection 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_column_projection 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_split_every 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_split_every 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_index 
[gw1] [ 60%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_index 
dask/dataframe/dask_expr/tests/test_groupby.py::test_split_out_automatically 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-True-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-False-0] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-False-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-False-1] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-False-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-True-0] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-True-0] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-True-1] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-True-1] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[True-False] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[True-False] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[True-True] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[True-True] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[False-False] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[False-False] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[False-True] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[False-True] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[None-False] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[None-False] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[None-True] 
[gw0] [ 60%] FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[None-True] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_std_raises_on_index 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_std_raises_on_index 
dask/dataframe/tests/test_arithmetics_reduction.py::test_std_raises_with_arrow_string_ea 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_std_raises_with_arrow_string_ea 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-int64[pyarrow]] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-int64[pyarrow]] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-float64[pyarrow]] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-float64[pyarrow]] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-Int64] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-Int64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-Int32] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-Int32] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-Float64] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-Float64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-UInt64] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[std-UInt64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-int64[pyarrow]] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-int64[pyarrow]] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-float64[pyarrow]] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-float64[pyarrow]] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-Int64] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-Int64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-Int32] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-Int32] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-Float64] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-Float64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-UInt64] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[var-UInt64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-int64[pyarrow]] 
[gw0] [ 60%] XFAIL dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-int64[pyarrow]] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-float64[pyarrow]] 
[gw0] [ 60%] XFAIL dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-float64[pyarrow]] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-Int64] 
[gw0] [ 60%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-Int64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-Int32] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-Int32] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-Float64] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-Float64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-UInt64] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[skew-UInt64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-int64[pyarrow]] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-int64[pyarrow]] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-float64[pyarrow]] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-float64[pyarrow]] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-Int64] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-Int64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-Int32] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-Int32] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-Float64] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-Float64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-UInt64] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_reductions_with_pandas_and_arrow_ea[kurtosis-UInt64] 
dask/dataframe/tests/test_arithmetics_reduction.py::test_quantile_arrow_dtype 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_arithmetics_reduction.py::test_quantile_arrow_dtype 
dask/dataframe/tests/test_boolean.py::test_meta 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_boolean.py::test_meta 
dask/dataframe/tests/test_boolean.py::test_ops 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_boolean.py::test_ops 
dask/dataframe/tests/test_categorical.py::test_concat_unions_categoricals 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_concat_unions_categoricals 
dask/dataframe/tests/test_categorical.py::test_unknown_categories_cudf 
[gw0] [ 61%] SKIPPED dask/dataframe/tests/test_categorical.py::test_unknown_categories_cudf 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-None-True] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-None-True] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-None-False] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-None-False] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-None-None] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-None-None] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-10-True] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-10-True] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-10-False] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-10-False] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-10-None] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-1-10-None] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-None-True] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-None-True] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-None-False] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-None-False] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-None-None] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-None-None] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-10-True] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-10-True] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-10-False] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-10-False] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-10-None] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[disk-4-10-None] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-None-True] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-None-True] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-None-False] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_split_out_automatically 
dask/dataframe/dask_expr/tests/test_groupby.py::test_split_out_sort_values_compute 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_split_out_sort_values_compute 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_repartition_to_one 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_repartition_to_one 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_apply 
[gw1] [ 61%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_apply 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_transform 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-None-False] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-None-None] 
[gw1] [ 61%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_transform 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_shift 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_shift 
dask/dataframe/dask_expr/tests/test_groupby.py::test_size 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_size 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric_only_lambda_caller 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-None-None] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-10-True] 
[gw1] [ 61%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric_only_lambda_caller 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-sum] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-sum] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-mean] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-mean] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-min] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-min] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-max] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-10-True] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-10-False] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-max] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-prod] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-prod] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-var] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-var] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-std] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-10-False] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-10-None] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-std] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-size] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-True-size] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-sum] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-sum] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-mean] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-1-10-None] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-None-True] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-mean] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-min] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-min] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-max] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-max] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-prod] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-prod] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-var] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-None-True] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-None-False] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-var] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-std] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-None-False] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-None-None] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-std] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-size] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[1-False-size] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-sum] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-sum] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-mean] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-mean] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-min] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-min] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-max] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-max] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-prod] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-prod] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-var] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-None-None] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-10-True] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-var] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-std] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-10-True] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-10-False] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-std] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-size] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-True-size] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-sum] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-10-False] 
dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-10-None] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-sum] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-mean] 
[gw0] [ 61%] XFAIL dask/dataframe/tests/test_categorical.py::test_unknown_categoricals[tasks-4-10-None] 
dask/dataframe/tests/test_categorical.py::test_categorize 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-mean] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-min] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-min] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-max] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorize 
dask/dataframe/tests/test_categorical.py::test_categorical_dtype 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_categorical_dtype 
dask/dataframe/tests/test_categorical.py::test_categorize_index 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_categorize_index 
dask/dataframe/tests/test_categorical.py::test_categorical_set_index[disk] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index[disk] 
dask/dataframe/tests/test_categorical.py::test_categorical_set_index[tasks] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-max] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-prod] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index[tasks] 
dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[1-1] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-prod] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-var] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[1-1] 
dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[1-3] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[1-3] 
dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[1-6] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-var] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-std] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[1-6] 
dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[3-1] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[3-1] 
dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[3-3] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-std] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-size] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[3-3] 
dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[3-6] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_single_agg_split_out[2-False-size] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-True] 
[gw1] [ 61%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-True] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-False] 
[gw1] [ 61%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-False] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-True] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[3-6] 
dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[6-1] 
[gw1] [ 61%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-True] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-False] 
[gw1] [ 61%] FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-False] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-spec0] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-spec0] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-spec1] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-spec1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-spec2] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-spec2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-spec3] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[6-1] 
dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[6-3] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-spec3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-spec4] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-spec4] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-sum] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-True-sum] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-spec0] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-spec0] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-spec1] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-spec1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-spec2] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-spec2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-spec3] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[6-3] 
dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[6-6] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-spec3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-spec4] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-spec4] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-sum] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[1-False-sum] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-spec0] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-spec0] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-spec1] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-spec1] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-spec2] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-spec2] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-spec3] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-spec3] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-spec4] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-spec4] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-sum] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[6-6] 
dask/dataframe/tests/test_categorical.py::test_repartition_on_categoricals[1] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-True-sum] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-spec0] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-spec0] 
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-spec1] 
[gw1] [ 61%] PASSED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_agg_split_out[2-False-spec1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>6] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_repartition_on_categoricals[1] 
dask/dataframe/tests/test_categorical.py::test_repartition_on_categoricals[4] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>0] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>1] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>2] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>3] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>4] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>5] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_repartition_on_categoricals[4] 
dask/dataframe/tests/test_categorical.py::test_categorical_accessor_presence 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::test_categorical_accessor_presence 
dask/dataframe/tests/test_categorical.py::test_categorize_nan 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>6] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>0] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>1] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>2] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_categorize_nan 
dask/dataframe/tests/test_categorical.py::test_return_type_known_categories 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>3] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>4] 
[gw0] [ 61%] FAILED dask/dataframe/tests/test_categorical.py::test_return_type_known_categories 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[categories-assert_array_index_eq-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[categories-assert_array_index_eq-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[categories-assert_array_index_eq-series1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[categories-assert_array_index_eq-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[categories-assert_array_index_eq-series2] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[categories-assert_array_index_eq-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[ordered-assert_eq-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[ordered-assert_eq-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[ordered-assert_eq-series1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[ordered-assert_eq-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[ordered-assert_eq-series2] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[ordered-assert_eq-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[codes-assert_array_index_eq-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[codes-assert_array_index_eq-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[codes-assert_array_index_eq-series1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[codes-assert_array_index_eq-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[codes-assert_array_index_eq-series2] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_properties[codes-assert_array_index_eq-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[add_categories-kwargs0-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[add_categories-kwargs0-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[add_categories-kwargs0-series1] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>5] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[add_categories-kwargs0-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[add_categories-kwargs0-series2] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[add_categories-kwargs0-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs1-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs1-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs1-series1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs1-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs1-series2] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs1-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_unordered-kwargs2-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_unordered-kwargs2-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_unordered-kwargs2-series1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_unordered-kwargs2-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_unordered-kwargs2-series2] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>6] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_unordered-kwargs2-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs3-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs3-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs3-series1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs3-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs3-series2] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[as_ordered-kwargs3-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_categories-kwargs4-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_categories-kwargs4-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_categories-kwargs4-series1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_categories-kwargs4-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_categories-kwargs4-series2] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_categories-kwargs4-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[rename_categories-kwargs5-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[rename_categories-kwargs5-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[rename_categories-kwargs5-series1] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[var-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[rename_categories-kwargs5-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[rename_categories-kwargs5-series2] 
[gw1] [ 61%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[rename_categories-kwargs5-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[reorder_categories-kwargs6-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[reorder_categories-kwargs6-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[reorder_categories-kwargs6-series1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[reorder_categories-kwargs6-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[reorder_categories-kwargs6-series2] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[reorder_categories-kwargs6-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[set_categories-kwargs7-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[set_categories-kwargs7-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[set_categories-kwargs7-series1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[set_categories-kwargs7-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[set_categories-kwargs7-series2] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[set_categories-kwargs7-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_unused_categories-kwargs8-series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_unused_categories-kwargs8-series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_unused_categories-kwargs8-series1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_unused_categories-kwargs8-series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_unused_categories-kwargs8-series2] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_callable[remove_unused_categories-kwargs8-series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_categorical_empty 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_categorical_empty 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_unknown_categories[series0] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_unknown_categories[series0] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_unknown_categories[series1] 
[gw0] [ 61%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_unknown_categories[series1] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_unknown_categories[series2] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_unknown_categories[series2] 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_categorical_string_ops 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_categorical_string_ops 
dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_categorical_non_string_raises 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_categorical.py::TestCategoricalAccessor::test_categorical_non_string_raises 
dask/dataframe/tests/test_dataframe.py::test_Dataframe 
[gw1] [ 62%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>2] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>3] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_Dataframe 
dask/dataframe/tests/test_dataframe.py::test_head_tail 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_head_tail 
dask/dataframe/tests/test_dataframe.py::test_head_npartitions 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>4] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_head_npartitions 
dask/dataframe/tests/test_dataframe.py::test_head_npartitions_warn 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_head_npartitions_warn 
dask/dataframe/tests/test_dataframe.py::test_index_head 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_index_head 
dask/dataframe/tests/test_dataframe.py::test_Series 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_Series 
dask/dataframe/tests/test_dataframe.py::test_Index 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_Index 
dask/dataframe/tests/test_dataframe.py::test_axes 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_axes 
dask/dataframe/tests/test_dataframe.py::test_series_axes 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_series_axes 
dask/dataframe/tests/test_dataframe.py::test_attributes 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_attributes 
dask/dataframe/tests/test_dataframe.py::test_column_names 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_column_names 
dask/dataframe/tests/test_dataframe.py::test_columns_named_divisions_and_meta 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>5] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_columns_named_divisions_and_meta 
dask/dataframe/tests/test_dataframe.py::test_index_names 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_index_names 
dask/dataframe/tests/test_dataframe.py::test_rename_columns 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_rename_columns 
dask/dataframe/tests/test_dataframe.py::test_rename_series 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_rename_series 
dask/dataframe/tests/test_dataframe.py::test_rename_series_method 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>6] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_rename_series_method 
dask/dataframe/tests/test_dataframe.py::test_rename_series_method_2 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_rename_series_method_2 
dask/dataframe/tests/test_dataframe.py::test_describe_numeric[tdigest-test_values0] 
[gw1] [ 62%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>0] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>1] 
[gw1] [ 62%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>2] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>3] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_describe_numeric[tdigest-test_values0] 
dask/dataframe/tests/test_dataframe.py::test_describe_numeric[dask-test_values1] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>4] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>5] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>6] 
[gw1] [ 62%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[cov-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>0] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>1] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_describe_numeric[dask-test_values1] 
dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset0] 
[gw1] [ 62%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>2] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>3] 
[gw0] [ 62%] FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset0] 
dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset1] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>4] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>5] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>6] 
[gw0] [ 62%] FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset1] 
dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset2] 
[gw1] [ 62%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>0] 
[gw0] [ 62%] FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset2] 
dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset3] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>1] 
[gw0] [ 62%] FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset3] 
dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset4] 
[gw1] [ 62%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>2] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>3] 
[gw0] [ 62%] FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset4] 
dask/dataframe/tests/test_dataframe.py::test_describe[all-None-None-None] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>4] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>5] 
[gw1] [ 62%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>6] 
[gw0] [ 62%] FAILED dask/dataframe/tests/test_dataframe.py::test_describe[all-None-None-None] 
dask/dataframe/tests/test_dataframe.py::test_describe[include6-None-percentiles6-None] 
[gw1] [ 62%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[corr-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>0] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>1] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>2] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>3] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>4] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>5] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>6] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>0] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>1] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>1] 
[gw0] [ 62%] FAILED dask/dataframe/tests/test_dataframe.py::test_describe[include6-None-percentiles6-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>2] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>3] 
dask/dataframe/tests/test_dataframe.py::test_describe[include7-None-None-None] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>4] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>5] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>6] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[nunique-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>0] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>1] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>2] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>3] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>4] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>5] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>6] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>0] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>1] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>2] 
[gw0] [ 62%] FAILED dask/dataframe/tests/test_dataframe.py::test_describe[include7-None-None-None] 
dask/dataframe/tests/test_dataframe.py::test_describe[include8-None-percentiles8-None] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>3] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>4] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>5] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>6] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[first-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>0] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>1] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>2] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>3] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>4] 
[gw0] [ 62%] FAILED dask/dataframe/tests/test_dataframe.py::test_describe[include8-None-percentiles8-None] 
dask/dataframe/tests/test_dataframe.py::test_describe[None-exclude9-None-None] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>5] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>6] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>0] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>1] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>2] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>3] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>4] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>5] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>6] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[last-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>0] 
[gw0] [ 62%] FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-exclude9-None-None] 
dask/dataframe/tests/test_dataframe.py::test_describe[include10-None-None-None] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>1] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>2] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>3] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>4] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>5] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>6] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>0] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>1] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>2] 
[gw0] [ 62%] FAILED dask/dataframe/tests/test_dataframe.py::test_describe[include10-None-None-None] 
dask/dataframe/tests/test_dataframe.py::test_describe_without_datetime_is_numeric 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>3] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>4] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>5] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>6] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[prod-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_agg[disk] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_agg[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_agg[tasks] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_describe_without_datetime_is_numeric 
dask/dataframe/tests/test_dataframe.py::test_describe_empty 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_agg[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-True-<lambda>0] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_describe_empty 
dask/dataframe/tests/test_dataframe.py::test_describe_empty_tdigest 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_describe_empty_tdigest 
dask/dataframe/tests/test_dataframe.py::test_describe_for_possibly_unsorted_q 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_describe_for_possibly_unsorted_q 
dask/dataframe/tests/test_dataframe.py::test_cumulative 
[gw1] [ 62%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-True-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-False-<lambda>0] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-False-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-True-<lambda>1] 
[gw1] [ 62%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-True-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-False-<lambda>1] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-False-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-False-<lambda>2] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_cumulative 
dask/dataframe/tests/test_dataframe.py::test_cumulative_with_nans 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-False-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-True-<lambda>0] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_cumulative_with_nans 
dask/dataframe/tests/test_dataframe.py::test_cumulative_with_duplicate_columns 
[gw1] [ 62%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-True-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-False-<lambda>0] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-False-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-True-<lambda>1] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_cumulative_with_duplicate_columns 
dask/dataframe/tests/test_dataframe.py::test_cumulative_empty_partitions[func0] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_cumulative_empty_partitions[func0] 
dask/dataframe/tests/test_dataframe.py::test_cumulative_empty_partitions[func1] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_cumulative_empty_partitions[func1] 
dask/dataframe/tests/test_dataframe.py::test_cumulative_empty_partitions[func2] 
[gw1] [ 62%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-True-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-False-<lambda>1] 
[gw0] [ 62%] XFAIL dask/dataframe/tests/test_dataframe.py::test_cumulative_empty_partitions[func2] 
dask/dataframe/tests/test_dataframe.py::test_cumulative_empty_partitions[func3] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-False-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-False-<lambda>2] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-False-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_nunique[disk] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_nunique[disk] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_nunique[tasks] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_nunique[tasks] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_nunique_across_group_same_value[disk] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_nunique_across_group_same_value[disk] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_nunique_across_group_same_value[tasks] 
[gw0] [ 62%] XFAIL dask/dataframe/tests/test_dataframe.py::test_cumulative_empty_partitions[func3] 
dask/dataframe/tests/test_dataframe.py::test_dropna 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_nunique_across_group_same_value[tasks] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_propagates_names[disk] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_propagates_names[disk] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_propagates_names[tasks] 
[gw1] [ 62%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_propagates_names[tasks] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumsum-1] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_dropna 
dask/dataframe/tests/test_dataframe.py::test_clip[2-5] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_clip[2-5] 
dask/dataframe/tests/test_dataframe.py::test_clip[2.5-3.5] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_clip[2.5-3.5] 
dask/dataframe/tests/test_dataframe.py::test_clip_axis_0 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_clip_axis_0 
dask/dataframe/tests/test_dataframe.py::test_clip_axis_1 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_clip_axis_1 
dask/dataframe/tests/test_dataframe.py::test_squeeze 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_squeeze 
dask/dataframe/tests/test_dataframe.py::test_where_mask 
[gw1] [ 62%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumsum-1] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumsum-2] 
[gw1] [ 62%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumsum-2] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumprod-1] 
[gw1] [ 62%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumprod-1] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumprod-2] 
[gw1] [ 62%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumprod-2] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumcount-1] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_where_mask 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_multi_argument 
[gw1] [ 62%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumcount-1] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumcount-2] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partitions_multi_argument 
dask/dataframe/tests/test_dataframe.py::test_map_partitions 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partitions 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_type 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partitions_type 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_partition_info 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partitions_partition_info 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_names 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partitions_names 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_column_info 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partitions_column_info 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_method_names 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partitions_method_names 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_propagates_index_metadata 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partitions_propagates_index_metadata 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_keeps_kwargs_readable 
[gw0] [ 62%] XFAIL dask/dataframe/tests/test_dataframe.py::test_map_partitions_keeps_kwargs_readable 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_with_delayed_collection 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partitions_with_delayed_collection 
dask/dataframe/tests/test_dataframe.py::test_metadata_inference_single_partition_aligned_args 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_metadata_inference_single_partition_aligned_args 
dask/dataframe/tests/test_dataframe.py::test_align_dataframes 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_align_dataframes 
dask/dataframe/tests/test_dataframe.py::test_drop_duplicates[disk] 
[gw1] [ 62%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumcount-2] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumsum-1] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_drop_duplicates[disk] 
dask/dataframe/tests/test_dataframe.py::test_drop_duplicates[tasks] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_drop_duplicates[tasks] 
dask/dataframe/tests/test_dataframe.py::test_drop_duplicates_subset 
[gw1] [ 62%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumsum-1] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumsum-2] 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_drop_duplicates_subset 
dask/dataframe/tests/test_dataframe.py::test_get_partition 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_get_partition 
dask/dataframe/tests/test_dataframe.py::test_ndim 
[gw0] [ 62%] PASSED dask/dataframe/tests/test_dataframe.py::test_ndim 
dask/dataframe/tests/test_dataframe.py::test_dtype 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_dtype 
dask/dataframe/tests/test_dataframe.py::test_value_counts 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_value_counts 
dask/dataframe/tests/test_dataframe.py::test_value_counts_not_sorted 
[gw1] [ 63%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumsum-2] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumprod-1] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_value_counts_not_sorted 
dask/dataframe/tests/test_dataframe.py::test_value_counts_with_dropna 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_value_counts_with_dropna 
dask/dataframe/tests/test_dataframe.py::test_value_counts_with_normalize 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_value_counts_with_normalize 
dask/dataframe/tests/test_dataframe.py::test_value_counts_with_normalize_and_dropna[True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_value_counts_with_normalize_and_dropna[True] 
dask/dataframe/tests/test_dataframe.py::test_value_counts_with_normalize_and_dropna[False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_value_counts_with_normalize_and_dropna[False] 
dask/dataframe/tests/test_dataframe.py::test_unique 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_unique 
dask/dataframe/tests/test_dataframe.py::test_isin 
[gw1] [ 63%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumprod-1] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumprod-2] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_isin 
dask/dataframe/tests/test_dataframe.py::test_contains_frame 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_contains_frame 
dask/dataframe/tests/test_dataframe.py::test_len 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_len 
dask/dataframe/tests/test_dataframe.py::test_size 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_size 
dask/dataframe/tests/test_dataframe.py::test_shape 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_shape 
dask/dataframe/tests/test_dataframe.py::test_nbytes 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_nbytes 
dask/dataframe/tests/test_dataframe.py::test_quantile[0.3-tdigest] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile[0.3-tdigest] 
dask/dataframe/tests/test_dataframe.py::test_quantile[0.3-dask] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile[0.3-dask] 
dask/dataframe/tests/test_dataframe.py::test_quantile[0.5-tdigest] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile[0.5-tdigest] 
dask/dataframe/tests/test_dataframe.py::test_quantile[0.5-dask] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile[0.5-dask] 
dask/dataframe/tests/test_dataframe.py::test_quantile[0.9-tdigest] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile[0.9-tdigest] 
dask/dataframe/tests/test_dataframe.py::test_quantile[0.9-dask] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile[0.9-dask] 
dask/dataframe/tests/test_dataframe.py::test_quantile_missing[tdigest] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile_missing[tdigest] 
dask/dataframe/tests/test_dataframe.py::test_quantile_missing[dask] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile_missing[dask] 
dask/dataframe/tests/test_dataframe.py::test_empty_quantile[tdigest] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_empty_quantile[tdigest] 
dask/dataframe/tests/test_dataframe.py::test_empty_quantile[dask] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_empty_quantile[dask] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[None-tdigest-expected0] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[None-tdigest-expected0] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[None-dask-expected1] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[None-dask-expected1] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[True-tdigest-expected0] 
[gw1] [ 63%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumprod-2] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumcount-1] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[True-tdigest-expected0] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[True-dask-expected1] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[True-dask-expected1] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[False-tdigest-expected0] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[False-tdigest-expected0] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[False-dask-expected1] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_quantile[False-dask-expected1] 
dask/dataframe/tests/test_dataframe.py::test_quantile_datetime_numeric_only_false 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile_datetime_numeric_only_false 
dask/dataframe/tests/test_dataframe.py::test_quantile_for_possibly_unsorted_q 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile_for_possibly_unsorted_q 
dask/dataframe/tests/test_dataframe.py::test_quantile_tiny_partitions 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile_tiny_partitions 
dask/dataframe/tests/test_dataframe.py::test_quantile_trivial_partitions 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_quantile_trivial_partitions 
dask/dataframe/tests/test_dataframe.py::test_index 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_index 
dask/dataframe/tests/test_dataframe.py::test_assign 
[gw1] [ 63%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumcount-1] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumcount-2] 
[gw0] [ 63%] FAILED dask/dataframe/tests/test_dataframe.py::test_assign 
dask/dataframe/tests/test_dataframe.py::test_assign_callable 
[gw1] [ 63%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumcount-2] 
dask/dataframe/tests/test_groupby.py::test_series_groupby[disk] 
[gw0] [ 63%] FAILED dask/dataframe/tests/test_dataframe.py::test_assign_callable 
dask/dataframe/tests/test_dataframe.py::test_assign_dtypes 
[gw1] [ 63%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby[disk] 
dask/dataframe/tests/test_groupby.py::test_series_groupby[tasks] 
[gw1] [ 63%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby[tasks] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_errors[disk] 
[gw1] [ 63%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_errors[disk] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_errors[tasks] 
[gw1] [ 63%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_errors[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_index_array[disk] 
[gw0] [ 63%] FAILED dask/dataframe/tests/test_dataframe.py::test_assign_dtypes 
dask/dataframe/tests/test_dataframe.py::test_assign_pandas_series 
[gw1] [ 63%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_index_array[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_index_array[tasks] 
[gw1] [ 63%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_index_array[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_set_index[disk] 
[gw1] [ 63%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_set_index[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_set_index[tasks] 
[gw1] [ 63%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_set_index[tasks] 
dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[disk-True] 
[gw0] [ 63%] FAILED dask/dataframe/tests/test_dataframe.py::test_assign_pandas_series 
dask/dataframe/tests/test_dataframe.py::test_map 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_map 
dask/dataframe/tests/test_dataframe.py::test_concat 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_concat 
dask/dataframe/tests/test_dataframe.py::test_known_divisions 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_known_divisions 
dask/dataframe/tests/test_dataframe.py::test_unknown_divisions 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_unknown_divisions 
dask/dataframe/tests/test_dataframe.py::test_with_min_count 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_with_min_count 
dask/dataframe/tests/test_dataframe.py::test_align[inner] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_align[inner] 
dask/dataframe/tests/test_dataframe.py::test_align[outer] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_align[outer] 
dask/dataframe/tests/test_dataframe.py::test_align[left] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_align[left] 
dask/dataframe/tests/test_dataframe.py::test_align[right] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_align[right] 
dask/dataframe/tests/test_dataframe.py::test_align_axis[inner] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_align_axis[inner] 
dask/dataframe/tests/test_dataframe.py::test_align_axis[outer] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_align_axis[outer] 
dask/dataframe/tests/test_dataframe.py::test_align_axis[left] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_align_axis[left] 
dask/dataframe/tests/test_dataframe.py::test_align_axis[right] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_align_axis[right] 
dask/dataframe/tests/test_dataframe.py::test_combine 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_combine 
dask/dataframe/tests/test_dataframe.py::test_combine_first 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_combine_first 
dask/dataframe/tests/test_dataframe.py::test_combine_first_all_nans[f8-i8] 
[gw0] [ 63%] XFAIL dask/dataframe/tests/test_dataframe.py::test_combine_first_all_nans[f8-i8] 
dask/dataframe/tests/test_dataframe.py::test_combine_first_all_nans[f8-f4] 
[gw0] [ 63%] XFAIL dask/dataframe/tests/test_dataframe.py::test_combine_first_all_nans[f8-f4] 
dask/dataframe/tests/test_dataframe.py::test_combine_first_all_nans[datetime64[s]-datetime64[ns]] 
[gw0] [ 63%] XFAIL dask/dataframe/tests/test_dataframe.py::test_combine_first_all_nans[datetime64[s]-datetime64[ns]] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_picklable 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_picklable 
dask/dataframe/tests/test_dataframe.py::test_random_partitions 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_random_partitions 
dask/dataframe/tests/test_dataframe.py::test_series_round 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_series_round 
dask/dataframe/tests/test_dataframe.py::test_repartition 
[gw1] [ 63%] PASSED dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[disk-True] 
dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[disk-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition 
dask/dataframe/tests/test_dataframe.py::test_repartition_on_pandas_dataframe 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_on_pandas_dataframe 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-1-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-2-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-4-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-float-5-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-1-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-2-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-4-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>0-M8[ns]-5-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-1-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-2-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-4-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-4-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-4-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-5-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-5-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-float-5-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-1-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-1-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-2-True] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-2-False] 
[gw0] [ 63%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-4-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-4-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-5-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-5-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-1-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-1-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-1-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-2-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-2-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-4-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-4-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-5-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-5-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-2-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-1-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-1-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-2-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-2-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-4-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-4-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-5-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-5-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-4-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-1-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-1-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-1-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-1-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-2-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-2-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-4-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-4-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-4-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-4-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-5-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-5-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions[<lambda>1-M8[ns]-5-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-1kiB-2-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-1kiB-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-1kiB-2-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-1kiB-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-1kiB-5-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-1kiB-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-1kiB-5-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-1kiB-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-379-2-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-379-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-379-2-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-379-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-379-5-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-379-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-379-5-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>0-379-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-1kiB-2-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-1kiB-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-1kiB-2-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-1kiB-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-1kiB-5-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-1kiB-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-1kiB-5-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-1kiB-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-379-2-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-379-2-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-379-2-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-379-2-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-379-5-True] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-379-5-True] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-379-5-False] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size[<lambda>1-379-5-False] 
dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size_arg 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_partition_size_arg 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions_same_limits 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions_same_limits 
dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions_numeric_edge_case 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_npartitions_numeric_edge_case 
dask/dataframe/tests/test_dataframe.py::test_repartition_object_index 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_object_index 
dask/dataframe/tests/test_dataframe.py::test_repartition_datetime_tz_index 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_datetime_tz_index 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1D-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1D-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1D-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1D-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1D-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1D-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-7D-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-7D-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-7D-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-7D-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-7D-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-7D-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-28h-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-28h-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-28h-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-28h-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-28h-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-28h-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1h-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1h-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1h-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1h-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1h-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15-1h-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1D-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1D-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1D-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1D-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1D-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1D-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-7D-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-7D-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-7D-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-7D-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-7D-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-7D-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-28h-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-28h-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-28h-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-28h-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-28h-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-28h-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1h-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1h-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1h-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1h-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1h-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-04-15 12:37:01-1h-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-01-01 12:37:00-1D-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-01-01 12:37:00-1D-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-01-01 12:37:00-1D-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-01-01 12:37:00-1D-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-01-01 12:37:00-1D-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-01-01 12:37:00-1D-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01-2000-01-01 12:37:00-7D-1] 
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dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-1D-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-1D-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-7D-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-7D-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-7D-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-7D-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-7D-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-7D-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-28h-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-28h-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-28h-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-28h-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-28h-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-28h-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-1h-1] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-1h-1] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-1h-20] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-1h-20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-1h-243] 
[gw0] [ 64%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_repartition_freq[2000-01-01 12:30:00_1-2000-01-01 12:37:00-1h-243] 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq_divisions 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_freq_divisions 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq_errors 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_freq_errors 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq_month 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_freq_month 
dask/dataframe/tests/test_dataframe.py::test_repartition_freq_day 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_freq_day 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[M-MS] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[M-MS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[ME-MS] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[ME-MS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[MS-MS] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[MS-MS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2M-2MS] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2M-2MS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[Q-QS] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[Q-QS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[Q-FEB-QS-FEB] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[Q-FEB-QS-FEB] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2Q-2QS] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2Q-2QS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2Q-FEB-2QS-FEB] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2Q-FEB-2QS-FEB] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2QS-FEB-2QS-FEB] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2QS-FEB-2QS-FEB] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[BQ-BQS] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[BQ-BQS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2BQ-2BQS] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2BQ-2BQS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[SM-SMS] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[SM-SMS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[A-YS] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[A-YS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[Y-YS0] 
[gw0] [ 64%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[Y-YS0] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[A-JUN-YS-JUN] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[A-JUN-YS-JUN] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[Y-JUN-YS-JUN] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[Y-JUN-YS-JUN] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[BA-BYS] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[BA-BYS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2BA-2BYS] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[2BA-2BYS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[BY-BYS] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[BY-BYS] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[Y-YS1] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[Y-YS1] 
dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[freq20-expected_freq20] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_freq_to_period_start[freq20-expected_freq20] 
dask/dataframe/tests/test_dataframe.py::test_repartition_input_errors 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_repartition_input_errors 
dask/dataframe/tests/test_dataframe.py::test_embarrassingly_parallel_operations 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_embarrassingly_parallel_operations 
dask/dataframe/tests/test_dataframe.py::test_fillna 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_fillna 
dask/dataframe/tests/test_dataframe.py::test_ffill 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_ffill 
dask/dataframe/tests/test_dataframe.py::test_bfill 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_bfill 
dask/dataframe/tests/test_dataframe.py::test_delayed_roundtrip[True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_delayed_roundtrip[True] 
dask/dataframe/tests/test_dataframe.py::test_delayed_roundtrip[False] 
[gw1] [ 65%] PASSED dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[disk-False] 
dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[tasks-True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_delayed_roundtrip[False] 
dask/dataframe/tests/test_dataframe.py::test_from_delayed_lazy_if_meta_provided 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_from_delayed_lazy_if_meta_provided 
dask/dataframe/tests/test_dataframe.py::test_fillna_duplicate_index 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_fillna_duplicate_index 
dask/dataframe/tests/test_dataframe.py::test_fillna_multi_dataframe 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_fillna_multi_dataframe 
dask/dataframe/tests/test_dataframe.py::test_fillna_dask_dataframe_input 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_fillna_dask_dataframe_input 
dask/dataframe/tests/test_dataframe.py::test_ffill_bfill 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_ffill_bfill 
dask/dataframe/tests/test_dataframe.py::test_fillna_series_types 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_fillna_series_types 
dask/dataframe/tests/test_dataframe.py::test_sample 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_sample 
dask/dataframe/tests/test_dataframe.py::test_sample_without_replacement 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_sample_without_replacement 
dask/dataframe/tests/test_dataframe.py::test_sample_raises 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_sample_raises 
dask/dataframe/tests/test_dataframe.py::test_query 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_query 
dask/dataframe/tests/test_dataframe.py::test_select_dtypes[include0-None] 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_select_dtypes[include0-None] 
dask/dataframe/tests/test_dataframe.py::test_select_dtypes[None-exclude1] 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_select_dtypes[None-exclude1] 
dask/dataframe/tests/test_dataframe.py::test_select_dtypes[include2-exclude2] 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_select_dtypes[include2-exclude2] 
dask/dataframe/tests/test_dataframe.py::test_select_dtypes[include3-None] 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_select_dtypes[include3-None] 
dask/dataframe/tests/test_dataframe.py::test_deterministic_apply_concat_apply_names 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_deterministic_apply_concat_apply_names 
dask/dataframe/tests/test_dataframe.py::test_reduction_method 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_reduction_method 
dask/dataframe/tests/test_dataframe.py::test_reduction_method_split_every 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_reduction_method_split_every 
dask/dataframe/tests/test_dataframe.py::test_pipe 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_pipe 
dask/dataframe/tests/test_dataframe.py::test_gh_517 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_gh_517 
dask/dataframe/tests/test_dataframe.py::test_drop_axis_1 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_drop_axis_1 
dask/dataframe/tests/test_dataframe.py::test_drop_columns[columns0] 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_drop_columns[columns0] 
dask/dataframe/tests/test_dataframe.py::test_drop_columns[columns1] 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_drop_columns[columns1] 
dask/dataframe/tests/test_dataframe.py::test_gh580 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_gh580 
dask/dataframe/tests/test_dataframe.py::test_rename_dict 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_rename_dict 
dask/dataframe/tests/test_dataframe.py::test_rename_function 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_rename_function 
dask/dataframe/tests/test_dataframe.py::test_rename_index 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_rename_index 
dask/dataframe/tests/test_dataframe.py::test_to_timestamp 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_timestamp 
dask/dataframe/tests/test_dataframe.py::test_to_frame 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_frame 
dask/dataframe/tests/test_dataframe.py::test_to_dask_array_raises[False0] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_dask_array_raises[False0] 
dask/dataframe/tests/test_dataframe.py::test_to_dask_array_raises[False1] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_dask_array_raises[False1] 
dask/dataframe/tests/test_dataframe.py::test_to_dask_array_unknown[False] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_dask_array_unknown[False] 
dask/dataframe/tests/test_dataframe.py::test_to_dask_array_unknown[True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_dask_array_unknown[True] 
dask/dataframe/tests/test_dataframe.py::test_to_dask_array[lengths0-False-None] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_dask_array[lengths0-False-None] 
dask/dataframe/tests/test_dataframe.py::test_to_dask_array[True-False-None] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_dask_array[True-False-None] 
dask/dataframe/tests/test_dataframe.py::test_to_dask_array[True-False-meta2] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_dask_array[True-False-meta2] 
dask/dataframe/tests/test_dataframe.py::test_apply 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_apply 
dask/dataframe/tests/test_dataframe.py::test_apply_warns 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_apply_warns 
dask/dataframe/tests/test_dataframe.py::test_dataframe_map[None] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_map[None] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_map[ignore] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_map[ignore] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_map_raises 
[gw0] [ 65%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_dataframe_map_raises 
dask/dataframe/tests/test_dataframe.py::test_add_prefix 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_add_prefix 
dask/dataframe/tests/test_dataframe.py::test_add_suffix 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_add_suffix 
dask/dataframe/tests/test_dataframe.py::test_abs 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_abs 
dask/dataframe/tests/test_dataframe.py::test_round 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_round 
dask/dataframe/tests/test_dataframe.py::test_cov_dataframe[None] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_cov_dataframe[None] 
dask/dataframe/tests/test_dataframe.py::test_cov_dataframe[True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_cov_dataframe[True] 
dask/dataframe/tests/test_dataframe.py::test_cov_dataframe[False] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_cov_dataframe[False] 
dask/dataframe/tests/test_dataframe.py::test_cov_series 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_cov_series 
dask/dataframe/tests/test_dataframe.py::test_cov_gpu[None] 
[gw0] [ 65%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_cov_gpu[None] 
dask/dataframe/tests/test_dataframe.py::test_cov_gpu[True] 
[gw0] [ 65%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_cov_gpu[True] 
dask/dataframe/tests/test_dataframe.py::test_cov_gpu[False] 
[gw0] [ 65%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_cov_gpu[False] 
dask/dataframe/tests/test_dataframe.py::test_corr 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_corr 
dask/dataframe/tests/test_dataframe.py::test_corr_gpu 
[gw0] [ 65%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_corr_gpu 
dask/dataframe/tests/test_dataframe.py::test_corr_same_name 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_corr_same_name 
dask/dataframe/tests/test_dataframe.py::test_cov_corr_meta[1] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_cov_corr_meta[1] 
dask/dataframe/tests/test_dataframe.py::test_cov_corr_meta[2] 
[gw1] [ 65%] PASSED dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[tasks-True] 
dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[tasks-False] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_cov_corr_meta[2] 
dask/dataframe/tests/test_dataframe.py::test_cov_corr_stable 
[gw0] [ 65%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_cov_corr_stable 
dask/dataframe/tests/test_dataframe.py::test_cov_corr_mixed[None] 
[gw0] [ 65%] XFAIL dask/dataframe/tests/test_dataframe.py::test_cov_corr_mixed[None] 
dask/dataframe/tests/test_dataframe.py::test_cov_corr_mixed[True] 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_cov_corr_mixed[True] 
dask/dataframe/tests/test_dataframe.py::test_cov_corr_mixed[False] 
[gw0] [ 65%] XFAIL dask/dataframe/tests/test_dataframe.py::test_cov_corr_mixed[False] 
dask/dataframe/tests/test_dataframe.py::test_autocorr 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_autocorr 
dask/dataframe/tests/test_dataframe.py::test_apply_infer_columns 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_apply_infer_columns 
dask/dataframe/tests/test_dataframe.py::test_index_time_properties 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_index_time_properties 
dask/dataframe/tests/test_dataframe.py::test_nlargest_nsmallest 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_nlargest_nsmallest 
dask/dataframe/tests/test_dataframe.py::test_nlargest_nsmallest_raises 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_nlargest_nsmallest_raises 
dask/dataframe/tests/test_dataframe.py::test_reset_index 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_reset_index 
dask/dataframe/tests/test_dataframe.py::test_dataframe_compute_forward_kwargs 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_compute_forward_kwargs 
dask/dataframe/tests/test_dataframe.py::test_series_iter 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_series_iter 
dask/dataframe/tests/test_dataframe.py::test_dataframe_iterrows 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_iterrows 
dask/dataframe/tests/test_dataframe.py::test_dataframe_itertuples 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_itertuples 
dask/dataframe/tests/test_dataframe.py::test_dataframe_items[columns0] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_items[columns0] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_itertuples_with_index_false 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_itertuples_with_index_false 
dask/dataframe/tests/test_dataframe.py::test_dataframe_itertuples_with_name_none 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_itertuples_with_name_none 
dask/dataframe/tests/test_dataframe.py::test_astype 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_astype 
dask/dataframe/tests/test_dataframe.py::test_astype_categoricals 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_astype_categoricals 
dask/dataframe/tests/test_dataframe.py::test_astype_categoricals_known 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_astype_categoricals_known 
dask/dataframe/tests/test_dataframe.py::test_groupby_callable 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_groupby_callable 
dask/dataframe/tests/test_dataframe.py::test_methods_tokenize_differently 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_methods_tokenize_differently 
dask/dataframe/tests/test_dataframe.py::test_gh_1301 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_gh_1301 
dask/dataframe/tests/test_dataframe.py::test_timeseries_sorted 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_timeseries_sorted 
dask/dataframe/tests/test_dataframe.py::test_index_errors 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_index_errors 
dask/dataframe/tests/test_dataframe.py::test_index_nulls[None] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_index_nulls[None] 
dask/dataframe/tests/test_dataframe.py::test_index_nulls[null_value1] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_index_nulls[null_value1] 
dask/dataframe/tests/test_dataframe.py::test_index_nulls[null_value2] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_index_nulls[null_value2] 
dask/dataframe/tests/test_dataframe.py::test_set_index_with_index 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_set_index_with_index 
dask/dataframe/tests/test_dataframe.py::test_column_assignment 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_column_assignment 
dask/dataframe/tests/test_dataframe.py::test_array_assignment 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_array_assignment 
dask/dataframe/tests/test_dataframe.py::test_columns_assignment 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_columns_assignment 
dask/dataframe/tests/test_dataframe.py::test_attribute_assignment 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_attribute_assignment 
dask/dataframe/tests/test_dataframe.py::test_setitem_triggering_realign 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_setitem_triggering_realign 
dask/dataframe/tests/test_dataframe.py::test_inplace_operators 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_inplace_operators 
dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx0-True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx0-True] 
dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx0-False] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx0-False] 
dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx1-True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx1-True] 
dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx1-False] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx1-False] 
dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx2-True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx2-True] 
dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx2-False] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_idxmaxmin[idx2-False] 
dask/dataframe/tests/test_dataframe.py::test_idxmaxmin_numeric_only[idxmin] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_idxmaxmin_numeric_only[idxmin] 
dask/dataframe/tests/test_dataframe.py::test_idxmaxmin_numeric_only[idxmax] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_idxmaxmin_numeric_only[idxmax] 
dask/dataframe/tests/test_dataframe.py::test_idxmaxmin_empty_partitions 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_idxmaxmin_empty_partitions 
dask/dataframe/tests/test_dataframe.py::test_mode_numeric_only 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_mode_numeric_only 
dask/dataframe/tests/test_dataframe.py::test_getitem_meta 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_getitem_meta 
dask/dataframe/tests/test_dataframe.py::test_getitem_string_subclass 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_getitem_string_subclass 
dask/dataframe/tests/test_dataframe.py::test_getitem_column_types[list] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_getitem_column_types[list] 
dask/dataframe/tests/test_dataframe.py::test_getitem_column_types[array] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_getitem_column_types[array] 
dask/dataframe/tests/test_dataframe.py::test_getitem_column_types[Series] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_getitem_column_types[Series] 
dask/dataframe/tests/test_dataframe.py::test_getitem_column_types[Index] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_getitem_column_types[Index] 
dask/dataframe/tests/test_dataframe.py::test_getitem_with_bool_dataframe_as_key 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_getitem_with_bool_dataframe_as_key 
dask/dataframe/tests/test_dataframe.py::test_getitem_with_non_series 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_getitem_with_non_series 
dask/dataframe/tests/test_dataframe.py::test_ipython_completion 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_ipython_completion 
dask/dataframe/tests/test_dataframe.py::test_diff 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_diff 
dask/dataframe/tests/test_dataframe.py::test_shift 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_shift 
dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_DatetimeIndex[B-False] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_DatetimeIndex[B-False] 
dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_DatetimeIndex[D-True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_DatetimeIndex[D-True] 
dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_DatetimeIndex[h-True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_DatetimeIndex[h-True] 
dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_PeriodIndex[D-True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_PeriodIndex[D-True] 
dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_PeriodIndex[h-True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_PeriodIndex[h-True] 
dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_TimedeltaIndex 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_TimedeltaIndex 
dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_errors 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_shift_with_freq_errors 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-2-1] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-2-1] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-2-4] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-2-4] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-2-20] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-2-20] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-5-1] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-5-1] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-5-4] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-5-4] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-5-20] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[1-5-20] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-2-1] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-2-1] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-2-4] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-2-4] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-2-20] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-2-20] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-5-1] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-5-1] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-5-4] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-5-4] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-5-20] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[5-5-20] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-2-1] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-2-1] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-2-4] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-2-4] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-2-20] 
[gw1] [ 65%] PASSED dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[tasks-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[sum-disk-split_every] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-2-20] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-5-1] 
[gw1] [ 65%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[sum-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[sum-disk-split_out] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-5-1] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-5-4] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-5-4] 
dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-5-20] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_hash_split_unique[20-5-20] 
dask/dataframe/tests/test_dataframe.py::test_split_out_drop_duplicates[None] 
[gw1] [ 65%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[sum-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[sum-tasks-split_every] 
[gw1] [ 65%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[sum-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[sum-tasks-split_out] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_split_out_drop_duplicates[None] 
dask/dataframe/tests/test_dataframe.py::test_split_out_drop_duplicates[2] 
[gw1] [ 65%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[sum-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[mean-disk-split_every] 
[gw1] [ 65%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[mean-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[mean-disk-split_out] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_split_out_drop_duplicates[2] 
dask/dataframe/tests/test_dataframe.py::test_split_out_value_counts[None] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_split_out_value_counts[None] 
dask/dataframe/tests/test_dataframe.py::test_split_out_value_counts[2] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_split_out_value_counts[2] 
dask/dataframe/tests/test_dataframe.py::test_values 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_values 
dask/dataframe/tests/test_dataframe.py::test_values_extension_dtypes 
[gw1] [ 65%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[mean-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[mean-tasks-split_every] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_values_extension_dtypes 
dask/dataframe/tests/test_dataframe.py::test_copy 
[gw1] [ 65%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[mean-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[mean-tasks-split_out] 
[gw0] [ 65%] FAILED dask/dataframe/tests/test_dataframe.py::test_copy 
dask/dataframe/tests/test_dataframe.py::test_del 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_del 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_dataframe[True-True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_dataframe[True-True] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_dataframe[True-False] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_dataframe[True-False] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_dataframe[False-True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_dataframe[False-True] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_dataframe[False-False] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_dataframe[False-False] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_series[True-True] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_series[True-True] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_series[True-False] 
[gw0] [ 65%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_series[True-False] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_series[False-True] 
[gw1] [ 65%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[mean-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-disk-split_every] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_series[False-True] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_series[False-False] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_series[False-False] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_index[True] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_index[True] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_index[False] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_index[False] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_per_partition[True-True] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_per_partition[True-True] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_per_partition[True-False] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_per_partition[True-False] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_per_partition[False-True] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_per_partition[False-True] 
dask/dataframe/tests/test_dataframe.py::test_memory_usage_per_partition[False-False] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_memory_usage_per_partition[False-False] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[sum] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[sum] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[mean] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[mean] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[std] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[std] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[var] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[var] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[count] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[count] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[min] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[min] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[max] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[max] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[idxmin] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[idxmin] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[idxmax] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[idxmax] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[prod] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[prod] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[all] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[all] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[sem] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_reductions_arithmetic[sem] 
dask/dataframe/tests/test_dataframe.py::test_median 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_median 
dask/dataframe/tests/test_dataframe.py::test_median_approximate[dask] 
[gw1] [ 66%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-disk-split_out] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_median_approximate[dask] 
dask/dataframe/tests/test_dataframe.py::test_median_approximate[tdigest] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_median_approximate[tdigest] 
dask/dataframe/tests/test_dataframe.py::test_datetime_loc_open_slicing 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_datetime_loc_open_slicing 
dask/dataframe/tests/test_dataframe.py::test_to_datetime[False] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_datetime[False] 
dask/dataframe/tests/test_dataframe.py::test_to_datetime[True] 
[gw0] [ 66%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_to_datetime[True] 
dask/dataframe/tests/test_dataframe.py::test_to_timedelta 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_timedelta 
dask/dataframe/tests/test_dataframe.py::test_isna[values0] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_isna[values0] 
dask/dataframe/tests/test_dataframe.py::test_isna[values1] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_isna[values1] 
dask/dataframe/tests/test_dataframe.py::test_slice_on_filtered_boundary[0] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_slice_on_filtered_boundary[0] 
dask/dataframe/tests/test_dataframe.py::test_slice_on_filtered_boundary[9] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_slice_on_filtered_boundary[9] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_nonmonotonic 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_nonmonotonic 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_empty 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_empty 
dask/dataframe/tests/test_dataframe.py::test_with_boundary[-1-None-False-False-drop0] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_with_boundary[-1-None-False-False-drop0] 
dask/dataframe/tests/test_dataframe.py::test_with_boundary[-1-None-False-True-drop1] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_with_boundary[-1-None-False-True-drop1] 
dask/dataframe/tests/test_dataframe.py::test_with_boundary[None-3-False-False-drop2] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_with_boundary[None-3-False-False-drop2] 
dask/dataframe/tests/test_dataframe.py::test_with_boundary[None-3-True-False-drop3] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_with_boundary[None-3-True-False-drop3] 
dask/dataframe/tests/test_dataframe.py::test_with_boundary[-0.5-None-False-False-drop4] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_with_boundary[-0.5-None-False-False-drop4] 
dask/dataframe/tests/test_dataframe.py::test_with_boundary[-0.5-None-False-True-drop5] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_with_boundary[-0.5-None-False-True-drop5] 
dask/dataframe/tests/test_dataframe.py::test_with_boundary[-1.5-None-False-True-drop6] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_with_boundary[-1.5-None-False-True-drop6] 
dask/dataframe/tests/test_dataframe.py::test_with_boundary[None-3.5-False-False-drop7] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_with_boundary[None-3.5-False-False-drop7] 
dask/dataframe/tests/test_dataframe.py::test_with_boundary[None-3.5-True-False-drop8] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_with_boundary[None-3.5-True-False-drop8] 
dask/dataframe/tests/test_dataframe.py::test_with_boundary[None-2.5-False-False-drop9] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_with_boundary[None-2.5-False-False-drop9] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index0-0-9] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index0-0-9] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index1--1-None] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index1--1-None] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index2-None-10] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index2-None-10] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index3-None-None] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index3-None-None] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index4--1-None] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index4--1-None] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index5-None-2] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index5-None-2] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index6--2-3] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index6--2-3] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index7-None-None] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index7-None-None] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index8-left8-None] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index8-left8-None] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index9-None-right9] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index9-None-right9] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index10-left10-None] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index10-left10-None] 
dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index11-None-right11] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_boundary_slice_same[index11-None-right11] 
dask/dataframe/tests/test_dataframe.py::test_better_errors_object_reductions 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_better_errors_object_reductions 
dask/dataframe/tests/test_dataframe.py::test_sample_empty_partitions 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_sample_empty_partitions 
dask/dataframe/tests/test_dataframe.py::test_coerce 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_coerce 
dask/dataframe/tests/test_dataframe.py::test_bool 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_bool 
dask/dataframe/tests/test_dataframe.py::test_cumulative_multiple_columns 
[gw1] [ 66%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-tasks-split_every] 
[gw0] [ 66%] FAILED dask/dataframe/tests/test_dataframe.py::test_cumulative_multiple_columns 
dask/dataframe/tests/test_dataframe.py::test_map_partition_array[asarray] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partition_array[asarray] 
dask/dataframe/tests/test_dataframe.py::test_map_partition_array[func1] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partition_array[func1] 
dask/dataframe/tests/test_dataframe.py::test_map_partition_sparse 
[gw0] [ 66%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_map_partition_sparse 
dask/dataframe/tests/test_dataframe.py::test_mixed_dask_array_operations 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_mixed_dask_array_operations 
dask/dataframe/tests/test_dataframe.py::test_mixed_dask_array_operations_errors 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_mixed_dask_array_operations_errors 
dask/dataframe/tests/test_dataframe.py::test_mixed_dask_array_multi_dimensional 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_mixed_dask_array_multi_dimensional 
dask/dataframe/tests/test_dataframe.py::test_meta_raises 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_meta_raises 
dask/dataframe/tests/test_dataframe.py::test_meta_nonempty_uses_meta_value_if_provided 
[gw0] [ 66%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_meta_nonempty_uses_meta_value_if_provided 
dask/dataframe/tests/test_dataframe.py::test_dask_dataframe_holds_scipy_sparse_containers[array] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dask_dataframe_holds_scipy_sparse_containers[array] 
dask/dataframe/tests/test_dataframe.py::test_dask_dataframe_holds_scipy_sparse_containers[matrix] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dask_dataframe_holds_scipy_sparse_containers[matrix] 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_delays_large_inputs 
[gw1] [ 66%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-tasks-split_out] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_map_partitions_delays_large_inputs 
dask/dataframe/tests/test_dataframe.py::test_partitions_indexer 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_partitions_indexer 
dask/dataframe/tests/test_dataframe.py::test_mod_eq 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_mod_eq 
dask/dataframe/tests/test_dataframe.py::test_setitem 
[gw0] [ 66%] FAILED dask/dataframe/tests/test_dataframe.py::test_setitem 
dask/dataframe/tests/test_dataframe.py::test_setitem_with_bool_dataframe_as_key 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_setitem_with_bool_dataframe_as_key 
dask/dataframe/tests/test_dataframe.py::test_setitem_with_bool_series_as_key 
[gw1] [ 66%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[min-disk-split_every] 
[gw0] [ 66%] FAILED dask/dataframe/tests/test_dataframe.py::test_setitem_with_bool_series_as_key 
dask/dataframe/tests/test_dataframe.py::test_setitem_with_numeric_column_name_raises_not_implemented 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_setitem_with_numeric_column_name_raises_not_implemented 
dask/dataframe/tests/test_dataframe.py::test_broadcast 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_broadcast 
dask/dataframe/tests/test_dataframe.py::test_scalar_with_array 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_scalar_with_array 
dask/dataframe/tests/test_dataframe.py::test_has_parallel_type 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_has_parallel_type 
dask/dataframe/tests/test_dataframe.py::test_map_index 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[min-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[min-disk-split_out] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_index 
dask/dataframe/tests/test_dataframe.py::test_assign_index 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_assign_index 
dask/dataframe/tests/test_dataframe.py::test_index_divisions 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_index_divisions 
dask/dataframe/tests/test_dataframe.py::test_replace 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_replace 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_delays_lists 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_map_partitions_delays_lists 
dask/dataframe/tests/test_dataframe.py::test_dtype_cast 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[min-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[min-tasks-split_every] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[min-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[min-tasks-split_out] 
[gw0] [ 66%] FAILED dask/dataframe/tests/test_dataframe.py::test_dtype_cast 
dask/dataframe/tests/test_dataframe.py::test_series_map[False-False-1-1] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_series_map[False-False-1-1] 
dask/dataframe/tests/test_dataframe.py::test_series_map[False-False-1-4] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[min-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[max-disk-split_every] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[False-False-1-4] 
dask/dataframe/tests/test_dataframe.py::test_series_map[False-False-3-1] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[max-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[max-disk-split_out] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[False-False-3-1] 
dask/dataframe/tests/test_dataframe.py::test_series_map[False-False-3-4] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[False-False-3-4] 
dask/dataframe/tests/test_dataframe.py::test_series_map[False-True-1-1] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_series_map[False-True-1-1] 
dask/dataframe/tests/test_dataframe.py::test_series_map[False-True-1-4] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[max-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[max-tasks-split_every] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[False-True-1-4] 
dask/dataframe/tests/test_dataframe.py::test_series_map[False-True-3-1] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[max-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[max-tasks-split_out] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[False-True-3-1] 
dask/dataframe/tests/test_dataframe.py::test_series_map[False-True-3-4] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[False-True-3-4] 
dask/dataframe/tests/test_dataframe.py::test_series_map[True-False-1-1] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_series_map[True-False-1-1] 
dask/dataframe/tests/test_dataframe.py::test_series_map[True-False-1-4] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[max-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[count-disk-split_every] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[count-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[count-disk-split_out] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[True-False-1-4] 
dask/dataframe/tests/test_dataframe.py::test_series_map[True-False-3-1] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[True-False-3-1] 
dask/dataframe/tests/test_dataframe.py::test_series_map[True-False-3-4] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[count-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[count-tasks-split_every] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[True-False-3-4] 
dask/dataframe/tests/test_dataframe.py::test_series_map[True-True-1-1] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_series_map[True-True-1-1] 
dask/dataframe/tests/test_dataframe.py::test_series_map[True-True-1-4] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[count-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[count-tasks-split_out] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[True-True-1-4] 
dask/dataframe/tests/test_dataframe.py::test_series_map[True-True-3-1] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[True-True-3-1] 
dask/dataframe/tests/test_dataframe.py::test_series_map[True-True-3-4] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[count-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[size-disk-split_every] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[size-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[size-disk-split_out] 
[gw0] [ 66%] XFAIL dask/dataframe/tests/test_dataframe.py::test_series_map[True-True-3-4] 
dask/dataframe/tests/test_dataframe.py::test_dataframe_explode 
[gw0] [ 66%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_dataframe_explode 
dask/dataframe/tests/test_dataframe.py::test_series_explode 
[gw0] [ 66%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_series_explode 
dask/dataframe/tests/test_dataframe.py::test_pop 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_pop 
dask/dataframe/tests/test_dataframe.py::test_nunique[0-True] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_nunique[0-True] 
dask/dataframe/tests/test_dataframe.py::test_nunique[0-False] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[size-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[size-tasks-split_every] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_nunique[0-False] 
dask/dataframe/tests/test_dataframe.py::test_nunique[1-True] 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[size-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[size-tasks-split_out] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_nunique[1-True] 
dask/dataframe/tests/test_dataframe.py::test_nunique[1-False] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_nunique[1-False] 
dask/dataframe/tests/test_dataframe.py::test_iter 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_iter 
dask/dataframe/tests/test_dataframe.py::test_dataframe_groupby_cumsum_agg_empty_partitions 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_groupby_cumsum_agg_empty_partitions 
dask/dataframe/tests/test_dataframe.py::test_dataframe_groupby_cumprod_agg_empty_partitions 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_groupby_cumprod_agg_empty_partitions 
dask/dataframe/tests/test_dataframe.py::test_attrs_dataframe 
[gw0] [ 66%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_attrs_dataframe 
dask/dataframe/tests/test_dataframe.py::test_attrs_series 
[gw0] [ 66%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_attrs_series 
dask/dataframe/tests/test_dataframe.py::test_join_series 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_join_series 
dask/dataframe/tests/test_dataframe.py::test_assign_na_float_columns 
[gw1] [ 66%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[size-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[std-disk-split_every] 
[gw0] [ 66%] FAILED dask/dataframe/tests/test_dataframe.py::test_assign_na_float_columns 
dask/dataframe/tests/test_dataframe.py::test_assign_no_warning_fragmented 
[gw1] [ 66%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[std-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[std-disk-split_out] 
[gw0] [ 66%] FAILED dask/dataframe/tests/test_dataframe.py::test_assign_no_warning_fragmented 
dask/dataframe/tests/test_dataframe.py::test_dot 
[gw1] [ 66%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[std-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[std-tasks-split_every] 
[gw0] [ 66%] FAILED dask/dataframe/tests/test_dataframe.py::test_dot 
dask/dataframe/tests/test_dataframe.py::test_dot_nan 
[gw1] [ 66%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[std-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[std-tasks-split_out] 
[gw0] [ 66%] FAILED dask/dataframe/tests/test_dataframe.py::test_dot_nan 
dask/dataframe/tests/test_dataframe.py::test_use_of_weakref_proxy 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_use_of_weakref_proxy 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-False-series0] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-False-series0] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-False-series1] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-False-series1] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-False-series2] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-False-series2] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-False-series3] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-False-series3] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-False-series4] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-False-series4] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-True-series0] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-True-series0] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-True-series1] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-True-series1] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-True-series2] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-True-series2] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-True-series3] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-True-series3] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-True-series4] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Series-True-series4] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-False-series0] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-False-series0] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-False-series1] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-False-series1] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-False-series2] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-False-series2] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-False-series3] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-False-series3] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-False-series4] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-False-series4] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-True-series0] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-True-series0] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-True-series1] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-True-series1] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-True-series2] 
[gw1] [ 66%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[std-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[var-disk-split_every] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-True-series2] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-True-series3] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-True-series3] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-True-series4] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_numeric[Index-True-series4] 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_dt64 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_dt64 
dask/dataframe/tests/test_dataframe.py::test_index_is_monotonic_dt64 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_index_is_monotonic_dt64 
dask/dataframe/tests/test_dataframe.py::test_is_monotonic_empty_partitions 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_is_monotonic_empty_partitions 
dask/dataframe/tests/test_dataframe.py::test_from_dict[2-columns-int] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_from_dict[2-columns-int] 
dask/dataframe/tests/test_dataframe.py::test_from_dict[2-columns-float] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_from_dict[2-columns-float] 
dask/dataframe/tests/test_dataframe.py::test_from_dict[2-index-int] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_from_dict[2-index-int] 
dask/dataframe/tests/test_dataframe.py::test_from_dict[2-index-float] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_from_dict[2-index-float] 
dask/dataframe/tests/test_dataframe.py::test_from_dict[5-columns-int] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_from_dict[5-columns-int] 
dask/dataframe/tests/test_dataframe.py::test_from_dict[5-columns-float] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_from_dict[5-columns-float] 
dask/dataframe/tests/test_dataframe.py::test_from_dict[5-index-int] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_from_dict[5-index-int] 
dask/dataframe/tests/test_dataframe.py::test_from_dict[5-index-float] 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_from_dict[5-index-float] 
dask/dataframe/tests/test_dataframe.py::test_from_dict_raises 
[gw0] [ 66%] PASSED dask/dataframe/tests/test_dataframe.py::test_from_dict_raises 
dask/dataframe/tests/test_dataframe.py::test_empty 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_empty 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_extension_dtype[int64[pyarrow]] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_pyarrow_extension_dtype[int64[pyarrow]] 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_extension_dtype[int32[pyarrow]] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_pyarrow_extension_dtype[int32[pyarrow]] 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_extension_dtype[float64[pyarrow]] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_pyarrow_extension_dtype[float64[pyarrow]] 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_extension_dtype[float32[pyarrow]] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_pyarrow_extension_dtype[float32[pyarrow]] 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_extension_dtype[uint8[pyarrow]] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_pyarrow_extension_dtype[uint8[pyarrow]] 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_decimal_extension_dtype 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_pyarrow_decimal_extension_dtype 
dask/dataframe/tests/test_dataframe.py::test_to_backend 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_to_backend 
dask/dataframe/tests/test_dataframe.py::test_transform_getitem_works[max] 
[gw0] [ 67%] FAILED dask/dataframe/tests/test_dataframe.py::test_transform_getitem_works[max] 
dask/dataframe/tests/test_dataframe.py::test_transform_getitem_works[sum] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[var-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[var-disk-split_out] 
[gw0] [ 67%] FAILED dask/dataframe/tests/test_dataframe.py::test_transform_getitem_works[sum] 
dask/dataframe/tests/test_dataframe.py::test_mask_where_array_like[df0-cond0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_mask_where_array_like[df0-cond0] 
dask/dataframe/tests/test_dataframe.py::test_mask_where_array_like[df1-cond1] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_mask_where_array_like[df1-cond1] 
dask/dataframe/tests/test_dataframe.py::test_mask_where_array_like[df2-cond2] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_mask_where_array_like[df2-cond2] 
dask/dataframe/tests/test_dataframe.py::test_mask_where_array_like[df3-cond3] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_mask_where_array_like[df3-cond3] 
dask/dataframe/tests/test_dataframe.py::test_mask_where_array_like[df4-cond4] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_mask_where_array_like[df4-cond4] 
dask/dataframe/tests/test_dataframe.py::test_mask_where_callable 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_mask_where_callable 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_schema_dispatch 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_pyarrow_schema_dispatch 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_schema_dispatch_preserves_index[True] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_pyarrow_schema_dispatch_preserves_index[True] 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_schema_dispatch_preserves_index[False] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_pyarrow_schema_dispatch_preserves_index[False] 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_conversion_dispatch[True] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_pyarrow_conversion_dispatch[True] 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_conversion_dispatch[False] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_pyarrow_conversion_dispatch[False] 
dask/dataframe/tests/test_dataframe.py::test_pyarrow_conversion_dispatch_cudf 
[gw0] [ 67%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_pyarrow_conversion_dispatch_cudf 
dask/dataframe/tests/test_dataframe.py::test_enforce_runtime_divisions 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_enforce_runtime_divisions 
dask/dataframe/tests/test_dataframe.py::test_preserve_ts_unit_in_meta_creation 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_preserve_ts_unit_in_meta_creation 
dask/dataframe/tests/test_dataframe.py::test_from_xarray 
[gw0] [ 67%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_from_xarray 
dask/dataframe/tests/test_dataframe.py::test_from_xarray_string_conversion 
[gw0] [ 67%] SKIPPED dask/dataframe/tests/test_dataframe.py::test_from_xarray_string_conversion 
dask/dataframe/tests/test_dataframe.py::test_dataframe_into_delayed 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_dataframe_into_delayed 
dask/dataframe/tests/test_dataframe.py::test_array_to_df_conversion 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_array_to_df_conversion 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_always_series[1] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partitions_always_series[1] 
dask/dataframe/tests/test_dataframe.py::test_map_partitions_always_series[5] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_map_partitions_always_series[5] 
dask/dataframe/tests/test_dataframe.py::test_partitions_are_plain_scalars 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_partitions_are_plain_scalars 
dask/dataframe/tests/test_dataframe.py::test_datetime_partitions_are_plain_scalars 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_datetime_partitions_are_plain_scalars 
dask/dataframe/tests/test_dataframe.py::test_loc_partitions_are_plain_scalars 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_dataframe.py::test_loc_partitions_are_plain_scalars 
dask/dataframe/tests/test_extensions.py::test_register_extension_type 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_extensions.py::test_register_extension_type 
dask/dataframe/tests/test_extensions.py::test_reduction 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_extensions.py::test_reduction 
dask/dataframe/tests/test_extensions.py::test_scalar 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_extensions.py::test_scalar 
dask/dataframe/tests/test_groupby.py::test_groupby_internal_repr_xfail[disk] 
[gw0] [ 67%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_internal_repr_xfail[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_internal_repr_xfail[tasks] 
[gw0] [ 67%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_internal_repr_xfail[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_error[disk] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_error[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_error[tasks] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_error[tasks] 
dask/dataframe/tests/test_groupby.py::test_full_groupby[disk] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby[disk] 
dask/dataframe/tests/test_groupby.py::test_full_groupby[tasks] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby[tasks] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-True-<lambda>0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-True-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-True-<lambda>1] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-True-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-True-<lambda>2] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-True-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-True-<lambda>3] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[var-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[var-tasks-split_every] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-True-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-True-<lambda>4] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-True-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-False-<lambda>0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-False-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-False-<lambda>1] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-False-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-False-<lambda>2] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-False-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-False-<lambda>3] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-False-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-False-<lambda>4] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[disk-False-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-True-<lambda>0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-True-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-True-<lambda>1] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[var-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[var-tasks-split_out] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-True-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-True-<lambda>2] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-True-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-True-<lambda>3] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-True-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-True-<lambda>4] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-True-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-False-<lambda>0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-False-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-False-<lambda>1] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-False-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-False-<lambda>2] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-False-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-False-<lambda>3] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-False-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-False-<lambda>4] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_full_groupby_multilevel[tasks-False-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_dir[disk] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_dir[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_dir[tasks] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_dir[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_on_index[disk-sync] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[var-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[cov-disk-split_every] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[cov-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[cov-disk-split_out] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[cov-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[cov-tasks-split_every] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[cov-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[cov-tasks-split_out] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[cov-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[corr-disk-split_every] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[corr-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[corr-disk-split_out] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[corr-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[corr-tasks-split_every] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[corr-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[corr-tasks-split_out] 
[gw1] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[corr-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[nunique-disk-split_every] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[nunique-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[nunique-disk-split_out] 
[gw0] [ 67%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_on_index[disk-sync] 
dask/dataframe/tests/test_groupby.py::test_groupby_on_index[disk-threads] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[nunique-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[nunique-tasks-split_every] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[nunique-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[nunique-tasks-split_out] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[nunique-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[first-disk-split_every] 
[gw1] [ 67%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[first-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[first-disk-split_out] 
[gw1] [ 67%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[first-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[first-tasks-split_every] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[first-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[first-tasks-split_out] 
[gw0] [ 67%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_on_index[disk-threads] 
dask/dataframe/tests/test_groupby.py::test_groupby_on_index[tasks-sync] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[first-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[last-disk-split_every] 
[gw1] [ 67%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[last-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[last-disk-split_out] 
[gw1] [ 67%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[last-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[last-tasks-split_every] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[last-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[last-tasks-split_out] 
[gw0] [ 67%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_on_index[tasks-sync] 
dask/dataframe/tests/test_groupby.py::test_groupby_on_index[tasks-threads] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[last-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[prod-disk-split_every] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[prod-disk-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[prod-disk-split_out] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[prod-disk-split_out] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[prod-tasks-split_every] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[prod-tasks-split_every] 
dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[prod-tasks-split_out] 
[gw0] [ 67%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_on_index[tasks-threads] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>1] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>2] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>3] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>4] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>5] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>6] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>1] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[prod-tasks-split_out] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>2] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>3] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>1] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>4] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>5] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>2] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>6] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[sum-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>0] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>1] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>3] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>2] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>4] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>3] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>4] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>4] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>5] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>6] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>6] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>0] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>7] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>1] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>2] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>7] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>8] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>3] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>3] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>8] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>9] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>5] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>9] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>10] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>6] 
[gw0] [ 67%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[mean-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>0] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>10] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>11] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>1] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>2] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>0-<lambda>11] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>3] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>1] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>4] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>2] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>5] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>6] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>3] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>0] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>4] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>1] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>5] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>2] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>3] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>6] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>4] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>7] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>5] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>6] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>7] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>8] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[median-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>1] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>8] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>9] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>2] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>3] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>9] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>10] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>4] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>5] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>6] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>10] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>11] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>1] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>2] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[disk-<lambda>1-<lambda>11] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>0] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>3] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>4] 
[gw1] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>1] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>5] 
[gw0] [ 67%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>6] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>2] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[min-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>0] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>1] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>3] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>2] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>3] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>4] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>4] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>5] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>5] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>6] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>0] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>6] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>1] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>2] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>7] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>3] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>4] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>7] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>8] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>5] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>6] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[max-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>0] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>8] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>9] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>1] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>2] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>9] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>10] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>3] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>4] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>4] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>10] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>11] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>6] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>0] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>1] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>0-<lambda>11] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>0] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>2] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>3] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>1] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>4] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>5] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>2] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>6] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[count-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>0] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>1] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>2] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>3] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>3] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>4] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>4] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>5] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>6] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>5] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>0] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>1] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>6] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>2] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>3] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>4] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>4] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>7] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>6] 
[gw0] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[size-tasks-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>0] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>7] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>8] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>8] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>9] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>1] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>9] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>10] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>2] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>10] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>11] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>3] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>4] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>5] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle[tasks-<lambda>1-<lambda>11] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>0] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>6] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-disk-<lambda>6] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>0] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>1] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>1] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>2] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>2] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>3] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>4] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>3] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>5] 
[gw0] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_multilevel_getitem[std-tasks-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-a] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>4] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>5] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-c] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>0-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>0] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>1] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-d] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>2] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>3] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>4] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-d] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-sel3] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>5] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-sel4] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[disk-<lambda>1-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>0] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>1] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>2] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-sel4] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-a] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>3] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>4] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>5] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-c] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>0-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>0] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>1] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-d] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>2] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>3] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>4] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-d] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-sel3] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>5] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[tasks-<lambda>1-<lambda>5] 
dask/dataframe/tests/test_groupby.py::test_numeric_column_names[disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_numeric_column_names[disk] 
dask/dataframe/tests/test_groupby.py::test_numeric_column_names[tasks] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-sel4] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_numeric_column_names[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_apply_tasks[disk] 
[gw1] [ 68%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_apply_tasks[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_apply_tasks[tasks] 
[gw1] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_apply_tasks[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_multiprocessing[disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multiprocessing[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_multiprocessing[tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multiprocessing[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_normalize_by[disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_normalize_by[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_normalize_by[tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_normalize_by[tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[sum-disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[sum-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[sum-tasks] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-sel4] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-a] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[sum-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[mean-disk] 
[gw1] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[mean-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[mean-tasks] 
[gw1] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[mean-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[median-disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[median-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[median-tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[median-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[min-disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[min-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[min-tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[min-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[max-disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[max-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[max-tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[max-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[count-disk] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-c] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[count-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[count-tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[count-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[size-disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[size-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[size-tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[size-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[std-disk] 
[gw1] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[std-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[std-tasks] 
[gw1] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[std-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[var-disk] 
[gw1] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[var-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[var-tasks] 
[gw1] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[var-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[cov-disk] 
[gw1] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[cov-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[cov-tasks] 
[gw1] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[cov-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[corr-disk] 
[gw1] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[corr-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[corr-tasks] 
[gw1] [ 68%] XPASS dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[corr-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[nunique-disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[nunique-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[nunique-tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[nunique-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[first-disk] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-d] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[first-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[first-tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[first-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[last-disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[last-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[last-tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[last-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[prod-disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[prod-disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[prod-tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate__single_element_groups[prod-tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate_build_agg_args__reuse_of_intermediates[disk] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate_build_agg_args__reuse_of_intermediates[disk] 
dask/dataframe/tests/test_groupby.py::test_aggregate_build_agg_args__reuse_of_intermediates[tasks] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate_build_agg_args__reuse_of_intermediates[tasks] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[disk-2-1] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-d] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-sel3] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-sel4] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-sel4] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-a] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[disk-2-1] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[disk-2-8] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-c] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-d] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[disk-2-8] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[disk-32-1] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-d] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-sel3] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-sel4] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-sel4] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-a] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[disk-32-1] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[disk-32-8] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-c] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-d] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[disk-32-8] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[tasks-2-1] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-d] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-sel3] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-sel4] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-sel4] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-a] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[tasks-2-1] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[tasks-2-8] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-c] 
[gw1] [ 68%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[tasks-2-8] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[tasks-32-1] 
[gw0] [ 68%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-d] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-d] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-sel3] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-sel4] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[tasks-32-1] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[tasks-32-8] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-sel4] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-a] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-c] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate[tasks-32-8] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_sort[disk-True] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-d] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_sort[disk-True] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_sort[disk-False] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-d] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-sel3] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-sel4] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-sel4] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-a] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_sort[disk-False] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_sort[tasks-True] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-c] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-d] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_sort[tasks-True] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_sort[tasks-False] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-d] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-sel3] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-sel4] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_sort[tasks-False] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_defaults[disk] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_defaults[disk] 
dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_defaults[tasks] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_shuffle_aggregate_defaults[tasks] 
dask/dataframe/tests/test_groupby.py::test_aggregate_median[disk-a-spec0] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-sel4] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-a] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate_median[disk-a-spec0] 
dask/dataframe/tests/test_groupby.py::test_aggregate_median[disk-a-spec1] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate_median[disk-a-spec1] 
dask/dataframe/tests/test_groupby.py::test_aggregate_median[disk-keys1-spec0] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate_median[disk-keys1-spec0] 
dask/dataframe/tests/test_groupby.py::test_aggregate_median[disk-keys1-spec1] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate_median[disk-keys1-spec1] 
dask/dataframe/tests/test_groupby.py::test_aggregate_median[tasks-a-spec0] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-c] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate_median[tasks-a-spec0] 
dask/dataframe/tests/test_groupby.py::test_aggregate_median[tasks-a-spec1] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate_median[tasks-a-spec1] 
dask/dataframe/tests/test_groupby.py::test_aggregate_median[tasks-keys1-spec0] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate_median[tasks-keys1-spec0] 
dask/dataframe/tests/test_groupby.py::test_aggregate_median[tasks-keys1-spec1] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-d] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_aggregate_median[tasks-keys1-spec1] 
dask/dataframe/tests/test_groupby.py::test_ffill[disk-None-True] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[disk-None-True] 
dask/dataframe/tests/test_groupby.py::test_ffill[disk-None-False] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[disk-None-False] 
dask/dataframe/tests/test_groupby.py::test_ffill[disk-None-None] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-d] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[disk-None-None] 
dask/dataframe/tests/test_groupby.py::test_ffill[disk-1-True] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-sel3] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[disk-1-True] 
dask/dataframe/tests/test_groupby.py::test_ffill[disk-1-False] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[disk-1-False] 
dask/dataframe/tests/test_groupby.py::test_ffill[disk-1-None] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[disk-1-None] 
dask/dataframe/tests/test_groupby.py::test_ffill[disk-4-True] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-sel4] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[disk-4-True] 
dask/dataframe/tests/test_groupby.py::test_ffill[disk-4-False] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[disk-4-False] 
dask/dataframe/tests/test_groupby.py::test_ffill[disk-4-None] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[disk-4-None] 
dask/dataframe/tests/test_groupby.py::test_ffill[tasks-None-True] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-sel4] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_multi_character_column_name[disk] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[tasks-None-True] 
dask/dataframe/tests/test_groupby.py::test_ffill[tasks-None-False] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[tasks-None-False] 
dask/dataframe/tests/test_groupby.py::test_ffill[tasks-None-None] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[tasks-None-None] 
dask/dataframe/tests/test_groupby.py::test_ffill[tasks-1-True] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_multi_character_column_name[disk] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_multi_character_column_name[tasks] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[tasks-1-True] 
dask/dataframe/tests/test_groupby.py::test_ffill[tasks-1-False] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[tasks-1-False] 
dask/dataframe/tests/test_groupby.py::test_ffill[tasks-1-None] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[tasks-1-None] 
dask/dataframe/tests/test_groupby.py::test_ffill[tasks-4-True] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[tasks-4-True] 
dask/dataframe/tests/test_groupby.py::test_ffill[tasks-4-False] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_multi_character_column_name[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_unaligned_index[disk] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[tasks-4-False] 
dask/dataframe/tests/test_groupby.py::test_ffill[tasks-4-None] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_ffill[tasks-4-None] 
dask/dataframe/tests/test_groupby.py::test_bfill[disk-None-True] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[disk-None-True] 
dask/dataframe/tests/test_groupby.py::test_bfill[disk-None-False] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[disk-None-False] 
dask/dataframe/tests/test_groupby.py::test_bfill[disk-None-None] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_unaligned_index[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_unaligned_index[tasks] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[disk-None-None] 
dask/dataframe/tests/test_groupby.py::test_bfill[disk-1-True] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[disk-1-True] 
dask/dataframe/tests/test_groupby.py::test_bfill[disk-1-False] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[disk-1-False] 
dask/dataframe/tests/test_groupby.py::test_bfill[disk-1-None] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[disk-1-None] 
dask/dataframe/tests/test_groupby.py::test_bfill[disk-4-True] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[disk-4-True] 
dask/dataframe/tests/test_groupby.py::test_bfill[disk-4-False] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[disk-4-False] 
dask/dataframe/tests/test_groupby.py::test_bfill[disk-4-None] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_unaligned_index[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_string_label[disk] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_string_label[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_string_label[tasks] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_string_label[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[disk-cumsum] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[disk-4-None] 
dask/dataframe/tests/test_groupby.py::test_bfill[tasks-None-True] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[tasks-None-True] 
dask/dataframe/tests/test_groupby.py::test_bfill[tasks-None-False] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[tasks-None-False] 
dask/dataframe/tests/test_groupby.py::test_bfill[tasks-None-None] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[tasks-None-None] 
dask/dataframe/tests/test_groupby.py::test_bfill[tasks-1-True] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[disk-cumsum] 
dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[disk-cumprod] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[tasks-1-True] 
dask/dataframe/tests/test_groupby.py::test_bfill[tasks-1-False] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[tasks-1-False] 
dask/dataframe/tests/test_groupby.py::test_bfill[tasks-1-None] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[tasks-1-None] 
dask/dataframe/tests/test_groupby.py::test_bfill[tasks-4-True] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[disk-cumprod] 
dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[tasks-cumsum] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[tasks-4-True] 
dask/dataframe/tests/test_groupby.py::test_bfill[tasks-4-False] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[tasks-4-False] 
dask/dataframe/tests/test_groupby.py::test_bfill[tasks-4-None] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_bfill[tasks-4-None] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-1-<lambda>0] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[tasks-cumsum] 
dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[tasks-cumprod] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-1-<lambda>1] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-1-<lambda>2] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-1-<lambda>3] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[tasks-cumprod] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_agg_reduces[disk] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_agg_reduces[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_agg_reduces[tasks] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-1-<lambda>4] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_agg_reduces[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_single[disk] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_single[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_single[tasks] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_single[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[disk-a] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[disk-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[disk-slice_1] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-1-<lambda>4] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[disk-slice_1] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[disk-slice_2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-2-<lambda>0] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[disk-slice_2] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[disk-slice_3] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[disk-slice_3] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[tasks-a] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[tasks-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[tasks-slice_1] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[tasks-slice_1] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[tasks-slice_2] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[tasks-slice_2] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[tasks-slice_3] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_grouper_multiple[tasks-slice_3] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-cumprod] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-2-<lambda>1] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-cumprod] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-cumcount] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-2-<lambda>2] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-2-<lambda>3] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-cumcount] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-cumsum] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-2-<lambda>4] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-cumsum] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-var] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-1-<lambda>0] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-1-<lambda>1] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-1-<lambda>2] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-sum] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-1-<lambda>3] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-1-<lambda>3] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-sum] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-mean] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-2-<lambda>0] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-mean] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-count] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-count] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-size] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-2-<lambda>1] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-size] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-std] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-2-<lambda>2] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-min] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-2-<lambda>3] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-min] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-max] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-max] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-first] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-2-<lambda>4] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-first] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-last] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-last] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-prod] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[sum-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-1-<lambda>0] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-1-<lambda>1] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-prod] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-cumprod] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-1-<lambda>2] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-1-<lambda>3] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-cumprod] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-cumcount] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-1-<lambda>4] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-2-<lambda>0] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-cumcount] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-cumsum] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-2-<lambda>1] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-cumsum] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-var] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-2-<lambda>2] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-sum] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-2-<lambda>3] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-sum] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-mean] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-mean] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-count] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-2-<lambda>4] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-count] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-size] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-size] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-std] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-1-<lambda>0] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-1-<lambda>1] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-1-<lambda>2] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-1-<lambda>3] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-min] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-min] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-max] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-1-<lambda>4] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-max] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-first] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-2-<lambda>0] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-first] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-last] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-last] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-prod] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-2-<lambda>1] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-prod] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-min-group_args0] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-2-<lambda>2] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-min-group_args0] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-min-group_args1] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-min-group_args1] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-min-group_args2] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-2-<lambda>3] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-2-<lambda>4] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-min-group_args2] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-min-idx] 
[gw1] [ 69%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[mean-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>0] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-min-idx] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-mean-group_args0] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>1] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>2] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-mean-group_args0] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-mean-group_args1] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-mean-group_args1] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-mean-group_args2] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>3] 
[gw1] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-mean-group_args2] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-mean-idx] 
[gw1] [ 69%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-mean-idx] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-<lambda>-group_args0] 
[gw1] [ 69%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-<lambda>-group_args0] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-<lambda>-group_args1] 
[gw0] [ 69%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-<lambda>-group_args1] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-<lambda>-group_args2] 
[gw0] [ 69%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-<lambda>-group_args2] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-<lambda>-idx] 
[gw1] [ 69%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-<lambda>-idx] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-min-group_args0] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-min-group_args0] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-min-group_args1] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-min-group_args1] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-min-group_args2] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-min-group_args2] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-min-idx] 
[gw1] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>0] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>1] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-min-idx] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-mean-group_args0] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>2] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>3] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-mean-group_args0] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-mean-group_args1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-mean-group_args1] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-mean-group_args2] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-mean-group_args2] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-mean-idx] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-mean-idx] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-<lambda>-group_args0] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-<lambda>-group_args0] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-<lambda>-group_args1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-<lambda>-group_args1] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-<lambda>-group_args2] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-<lambda>-group_args2] 
dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-<lambda>-idx] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-<lambda>-idx] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[disk-pandas_spec0-dask_spec0-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[disk-pandas_spec0-dask_spec0-False] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[disk-pandas_spec1-dask_spec1-True] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[disk-pandas_spec1-dask_spec1-True] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[disk-pandas_spec2-dask_spec2-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[disk-pandas_spec2-dask_spec2-False] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[disk-pandas_spec3-dask_spec3-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[disk-pandas_spec3-dask_spec3-False] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[tasks-pandas_spec0-dask_spec0-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[tasks-pandas_spec0-dask_spec0-False] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[tasks-pandas_spec1-dask_spec1-True] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[tasks-pandas_spec1-dask_spec1-True] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[tasks-pandas_spec2-dask_spec2-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[tasks-pandas_spec2-dask_spec2-False] 
dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[tasks-pandas_spec3-dask_spec3-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum[tasks-pandas_spec3-dask_spec3-False] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[disk-mean-mean] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[disk-mean-mean] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[disk-pandas_spec1-dask_spec1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[disk-pandas_spec1-dask_spec1] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[disk-pandas_spec2-dask_spec2] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[disk-pandas_spec2-dask_spec2] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[tasks-mean-mean] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[tasks-mean-mean] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[tasks-pandas_spec1-dask_spec1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[tasks-pandas_spec1-dask_spec1] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[tasks-pandas_spec2-dask_spec2] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_agg_custom_mean[tasks-pandas_spec2-dask_spec2] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__name_clash_with_internal_same_column[disk] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__name_clash_with_internal_same_column[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__name_clash_with_internal_same_column[tasks] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__name_clash_with_internal_same_column[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__name_clash_with_internal_different_column[disk] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__name_clash_with_internal_different_column[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__name_clash_with_internal_different_column[tasks] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__name_clash_with_internal_different_column[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__mode[disk] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__mode[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__mode[tasks] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_agg_custom__mode[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_select_column_agg[disk-var] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_select_column_agg[disk-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_select_column_agg[disk-list] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_select_column_agg[disk-list] 
dask/dataframe/tests/test_groupby.py::test_groupby_select_column_agg[tasks-var] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_select_column_agg[tasks-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_select_column_agg[tasks-list] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_select_column_agg[tasks-list] 
dask/dataframe/tests/test_groupby.py::test_std_object_dtype[disk-var] 
[gw1] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>0] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>1] 
[gw0] [ 70%] XFAIL dask/dataframe/tests/test_groupby.py::test_std_object_dtype[disk-var] 
dask/dataframe/tests/test_groupby.py::test_std_object_dtype[disk-std] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>2] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>3] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
[gw0] [ 70%] XFAIL dask/dataframe/tests/test_groupby.py::test_std_object_dtype[disk-std] 
dask/dataframe/tests/test_groupby.py::test_std_object_dtype[disk-mean] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
[gw0] [ 70%] XFAIL dask/dataframe/tests/test_groupby.py::test_std_object_dtype[disk-mean] 
dask/dataframe/tests/test_groupby.py::test_std_object_dtype[disk-sum] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
[gw0] [ 70%] XFAIL dask/dataframe/tests/test_groupby.py::test_std_object_dtype[disk-sum] 
dask/dataframe/tests/test_groupby.py::test_std_object_dtype[tasks-var] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
[gw0] [ 70%] XFAIL dask/dataframe/tests/test_groupby.py::test_std_object_dtype[tasks-var] 
dask/dataframe/tests/test_groupby.py::test_std_object_dtype[tasks-std] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
[gw0] [ 70%] XFAIL dask/dataframe/tests/test_groupby.py::test_std_object_dtype[tasks-std] 
dask/dataframe/tests/test_groupby.py::test_std_object_dtype[tasks-mean] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
[gw0] [ 70%] XFAIL dask/dataframe/tests/test_groupby.py::test_std_object_dtype[tasks-mean] 
dask/dataframe/tests/test_groupby.py::test_std_object_dtype[tasks-sum] 
[gw1] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>0] 
[gw0] [ 70%] XFAIL dask/dataframe/tests/test_groupby.py::test_std_object_dtype[tasks-sum] 
dask/dataframe/tests/test_groupby.py::test_std_columns_int[disk] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_std_columns_int[disk] 
dask/dataframe/tests/test_groupby.py::test_std_columns_int[tasks] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_std_columns_int[tasks] 
dask/dataframe/tests/test_groupby.py::test_timeseries[disk] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>2] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_timeseries[disk] 
dask/dataframe/tests/test_groupby.py::test_timeseries[tasks] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>3] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_timeseries[tasks] 
dask/dataframe/tests/test_groupby.py::test_with_min_count[disk-0] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_with_min_count[disk-0] 
dask/dataframe/tests/test_groupby.py::test_with_min_count[disk-1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_with_min_count[disk-1] 
dask/dataframe/tests/test_groupby.py::test_with_min_count[disk-2] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_with_min_count[disk-2] 
dask/dataframe/tests/test_groupby.py::test_with_min_count[disk-3] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_with_min_count[disk-3] 
dask/dataframe/tests/test_groupby.py::test_with_min_count[tasks-0] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_with_min_count[tasks-0] 
dask/dataframe/tests/test_groupby.py::test_with_min_count[tasks-1] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_with_min_count[tasks-1] 
dask/dataframe/tests/test_groupby.py::test_with_min_count[tasks-2] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_with_min_count[tasks-2] 
dask/dataframe/tests/test_groupby.py::test_with_min_count[tasks-3] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_with_min_count[tasks-3] 
dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[disk-True] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[disk-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[disk-False] 
[gw1] [ 70%] RERUN dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[disk-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[disk-None] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[disk-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[tasks-True] 
[gw1] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-1-<lambda>0] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-1-<lambda>1] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-1-<lambda>2] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[tasks-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[tasks-False] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-1-<lambda>3] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-1-<lambda>4] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-2-<lambda>0] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[tasks-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[tasks-None] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-2-<lambda>1] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[tasks-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_cov[disk-columns0] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_cov[disk-columns0] 
dask/dataframe/tests/test_groupby.py::test_groupby_cov[disk-columns1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_cov[disk-columns1] 
dask/dataframe/tests/test_groupby.py::test_groupby_cov[disk-columns2] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-2-<lambda>2] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_cov[disk-columns2] 
dask/dataframe/tests/test_groupby.py::test_groupby_cov[disk-columns3] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_cov[disk-columns3] 
dask/dataframe/tests/test_groupby.py::test_groupby_cov[tasks-columns0] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_cov[tasks-columns0] 
dask/dataframe/tests/test_groupby.py::test_groupby_cov[tasks-columns1] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-2-<lambda>3] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_cov[tasks-columns1] 
dask/dataframe/tests/test_groupby.py::test_groupby_cov[tasks-columns2] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_cov[tasks-columns2] 
dask/dataframe/tests/test_groupby.py::test_groupby_cov[tasks-columns3] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_cov[tasks-columns3] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin[disk] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin[disk] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin[tasks] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin[tasks] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin_skipna[disk-True] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-2-<lambda>4] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin_skipna[disk-True] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin_skipna[disk-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin_skipna[disk-False] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin_skipna[tasks-True] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin_skipna[tasks-True] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin_skipna[tasks-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmin_skipna[tasks-False] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax[disk] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax[disk] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax[tasks] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax[tasks] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax_skipna[disk-True] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax_skipna[disk-True] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax_skipna[disk-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax_skipna[disk-False] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax_skipna[tasks-True] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax_skipna[tasks-True] 
dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax_skipna[tasks-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_df_groupby_idxmax_skipna[tasks-False] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin[disk] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin[disk] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin[tasks] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin[tasks] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin_skipna[disk-True] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin_skipna[disk-True] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin_skipna[disk-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin_skipna[disk-False] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin_skipna[tasks-True] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin_skipna[tasks-True] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin_skipna[tasks-False] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-1-<lambda>0] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmin_skipna[tasks-False] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax[disk] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax[disk] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax[tasks] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax[tasks] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax_skipna[disk-True] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax_skipna[disk-True] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax_skipna[disk-False] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax_skipna[disk-False] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax_skipna[tasks-True] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax_skipna[tasks-True] 
dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax_skipna[tasks-False] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-1-<lambda>1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_series_groupby_idxmax_skipna[tasks-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_unique[disk-uint8] 
[gw0] [ 70%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_unique[disk-uint8] 
dask/dataframe/tests/test_groupby.py::test_groupby_unique[disk-int32] 
[gw0] [ 70%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_unique[disk-int32] 
dask/dataframe/tests/test_groupby.py::test_groupby_unique[disk-int64] 
[gw0] [ 70%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_unique[disk-int64] 
dask/dataframe/tests/test_groupby.py::test_groupby_unique[tasks-uint8] 
[gw0] [ 70%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_unique[tasks-uint8] 
dask/dataframe/tests/test_groupby.py::test_groupby_unique[tasks-int32] 
[gw0] [ 70%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_unique[tasks-int32] 
dask/dataframe/tests/test_groupby.py::test_groupby_unique[tasks-int64] 
[gw0] [ 70%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_unique[tasks-int64] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-uint8-foo] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-uint8-foo] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-uint8-by1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-uint8-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-int32-foo] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-1-<lambda>2] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-int32-foo] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-int32-by1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-int32-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-int64-foo] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-int64-foo] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-int64-by1] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-1-<lambda>3] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[disk-int64-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-uint8-foo] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-uint8-foo] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-uint8-by1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-uint8-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-int32-foo] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-int32-foo] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-int32-by1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-1-<lambda>4] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-int32-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-int64-foo] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-int64-foo] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-int64-by1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts[tasks-int64-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts_10322[disk] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts_10322[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts_10322[tasks] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-2-<lambda>0] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts_10322[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_shift_series[disk] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_shift_series[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_shift_series[tasks] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_shift_series[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_shift_lazy_input[disk] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-2-<lambda>1] 
[gw0] [ 70%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_shift_lazy_input[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_shift_lazy_input[tasks] 
[gw0] [ 70%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_shift_lazy_input[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_shift_within_partition_sorting[disk] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-2-<lambda>2] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-2-<lambda>3] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_shift_within_partition_sorting[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_shift_within_partition_sorting[tasks] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-2-<lambda>4] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_shift_within_partition_sorting[tasks] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[min-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_shift_with_freq[disk] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-1-<lambda>1] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-1-<lambda>2] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-1-<lambda>3] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_shift_with_freq[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_shift_with_freq[tasks] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-1-<lambda>4] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-2-<lambda>0] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-2-<lambda>1] 
[gw0] [ 70%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_shift_with_freq[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[disk-<lambda>] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[disk-<lambda>] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[disk-sum0] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[disk-sum0] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[disk-sum1] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-2-<lambda>2] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[disk-sum1] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[disk-rank] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-2-<lambda>3] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[disk-rank] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[tasks-<lambda>] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[tasks-<lambda>] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[tasks-sum0] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[tasks-sum0] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[tasks-sum1] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[tasks-sum1] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[tasks-rank] 
[gw1] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-2-<lambda>4] 
[gw0] [ 70%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[tasks-rank] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-1] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-2] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-2] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-3] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-3] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-4] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-4] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-5] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-1-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-5] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-6] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-6] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-7] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-1-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-7] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-8] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-1-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-8] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-9] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-indexed-9] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-1] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-1-<lambda>3] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-1] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-2] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-2] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-3] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-1-<lambda>4] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-3] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-4] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-2-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-4] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-5] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-5] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-6] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-2-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-6] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-7] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-7] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-8] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-2-<lambda>2] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-8] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-9] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[disk-not_indexed-9] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-1] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-2] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-2-<lambda>3] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-2] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-3] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-3] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-4] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-4] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-5] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-5] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-6] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-6] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-7] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-2-<lambda>4] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-7] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-8] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-8] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-9] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-indexed-9] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-1] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-2] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[max-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-1-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-2] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-3] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-3] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-4] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-1-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-4] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-5] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-1-<lambda>2] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-5] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-6] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-1-<lambda>3] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-6] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-7] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-1-<lambda>4] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-7] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-8] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-2-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-8] 
dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-9] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_transform_ufunc_partitioning[tasks-not_indexed-9] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[disk-<lambda>-<lambda>0] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-2-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[disk-<lambda>-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[disk-<lambda>-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[disk-<lambda>-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[disk-<lambda>-<lambda>2] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[disk-<lambda>-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[disk-<lambda>-<lambda>3] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-2-<lambda>2] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[disk-<lambda>-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[tasks-<lambda>-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[tasks-<lambda>-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[tasks-<lambda>-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[tasks-<lambda>-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[tasks-<lambda>-<lambda>2] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-2-<lambda>3] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[tasks-<lambda>-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[tasks-<lambda>-<lambda>3] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categoricals[tasks-<lambda>-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[disk-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[disk-<lambda>1] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-2-<lambda>4] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[disk-<lambda>2] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[disk-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[disk-<lambda>3] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[disk-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[tasks-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[tasks-<lambda>1] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-1-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[tasks-<lambda>2] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[tasks-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[tasks-<lambda>3] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-1-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function[tasks-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_kwargs[disk-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_kwargs[disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_kwargs[disk-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_kwargs[disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_kwargs[tasks-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_kwargs[tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_kwargs[tasks-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_kwargs[tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_args[disk-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_args[disk-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_args[disk-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_args[disk-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_args[tasks-<lambda>0] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_args[tasks-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_args[tasks-<lambda>1] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_partial_function_unexpected_args[tasks-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_pandas[disk-False] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_pandas[disk-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_pandas[disk-True] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-1-<lambda>2] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_pandas[disk-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_pandas[tasks-False] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_pandas[tasks-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_pandas[tasks-True] 
[gw0] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_pandas[tasks-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-a-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-a-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-a-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-a-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-a-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-a-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-c-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-c-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-c-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-c-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-c-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-c-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-d-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-d-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-d-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-d-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-d-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-d-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by3-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by3-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by3-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by3-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by3-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by3-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by4-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by4-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by4-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by4-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by4-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by4-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by5-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by5-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by5-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by5-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by5-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-True-by5-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-a-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-a-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-a-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-a-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-a-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-a-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-c-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-c-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-c-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-c-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-c-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-c-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-d-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-d-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-d-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-d-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-d-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-d-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by3-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by3-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by3-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by3-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by3-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by3-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by4-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by4-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by4-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by4-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by4-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by4-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by5-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by5-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by5-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by5-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by5-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-False-by5-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-a-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-a-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-a-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-a-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-a-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-a-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-c-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-c-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-c-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-c-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-c-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-c-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-d-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-d-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-d-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-d-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-d-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-d-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by3-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by3-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by3-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by3-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by3-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by3-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by4-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by4-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by4-True] 
[gw1] [ 71%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-1-<lambda>2] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by4-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by4-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by4-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by5-False] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-1-<lambda>3] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by5-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by5-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by5-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by5-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[disk-None-by5-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-a-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-a-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-a-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-a-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-a-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-a-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-c-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-c-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-c-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-c-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-c-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-c-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-d-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-d-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-d-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-d-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-d-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-d-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by3-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by3-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by3-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by3-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by3-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by3-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by4-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by4-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by4-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by4-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by4-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by4-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by5-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by5-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by5-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by5-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by5-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-True-by5-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-a-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-a-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-a-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-a-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-a-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-a-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-c-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-c-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-c-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-c-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-c-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-c-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-d-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-d-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-d-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-d-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-d-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-d-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by3-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by3-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by3-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by3-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by3-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by3-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by4-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by4-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by4-True] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by4-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by4-None] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by4-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by5-False] 
[gw0] [ 71%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by5-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by5-True] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by5-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by5-None] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-False-by5-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-a-False] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-a-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-a-True] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-a-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-a-None] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-a-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-c-False] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-c-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-c-True] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-c-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-c-None] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-c-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-d-False] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-d-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-d-True] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-d-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-d-None] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-d-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by3-False] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by3-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by3-True] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by3-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by3-None] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by3-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by4-False] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by4-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by4-True] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by4-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by4-None] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by4-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by5-False] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by5-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by5-True] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by5-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by5-None] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_cudf[tasks-None-by5-None] 
dask/dataframe/tests/test_groupby.py::test_groupby_grouper_dispatch[disk-a] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_grouper_dispatch[disk-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_grouper_dispatch[disk-b] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_grouper_dispatch[disk-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_grouper_dispatch[tasks-a] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_grouper_dispatch[tasks-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_grouper_dispatch[tasks-b] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_grouper_dispatch[tasks-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_apply_cudf[disk-True] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_apply_cudf[disk-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_apply_cudf[disk-False] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_apply_cudf[disk-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_apply_cudf[tasks-True] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_apply_cudf[tasks-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_apply_cudf[tasks-False] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_apply_cudf[tasks-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_with_agg[disk-True] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-1-<lambda>4] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_with_agg[disk-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_with_agg[disk-False] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_with_agg[disk-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_with_agg[tasks-True] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_with_agg[tasks-True] 
dask/dataframe/tests/test_groupby.py::test_groupby_dropna_with_agg[tasks-False] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-2-<lambda>0] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_dropna_with_agg[tasks-False] 
dask/dataframe/tests/test_groupby.py::test_groupby_observed_with_agg[disk] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_observed_with_agg[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_observed_with_agg[tasks] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_observed_with_agg[tasks] 
dask/dataframe/tests/test_groupby.py::test_rounding_negative_var[disk] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_rounding_negative_var[disk] 
dask/dataframe/tests/test_groupby.py::test_rounding_negative_var[tasks] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_rounding_negative_var[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column0-2] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-2-<lambda>1] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column0-2] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column0-3] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column0-3] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column1-2] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-2-<lambda>2] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column1-2] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column1-3] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-2-<lambda>3] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column1-3] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column2-2] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column2-2] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column2-3] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-2-<lambda>4] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column2-3] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column0-2] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column0-2] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column0-3] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[count-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-1-<lambda>0] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column0-3] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column1-2] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-1-<lambda>1] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column1-2] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column1-3] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-1-<lambda>2] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column1-3] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column2-2] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-1-<lambda>3] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column2-2] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column2-3] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-1-<lambda>4] 
[gw0] [ 72%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column2-3] 
dask/dataframe/tests/test_groupby.py::test_groupby_large_ints_exception[disk-pandas] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_large_ints_exception[disk-pandas] 
dask/dataframe/tests/test_groupby.py::test_groupby_large_ints_exception[disk-cudf] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_large_ints_exception[disk-cudf] 
dask/dataframe/tests/test_groupby.py::test_groupby_large_ints_exception[tasks-pandas] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_large_ints_exception[tasks-pandas] 
dask/dataframe/tests/test_groupby.py::test_groupby_large_ints_exception[tasks-cudf] 
[gw0] [ 72%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_large_ints_exception[tasks-cudf] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-count-a] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-2-<lambda>0] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-count-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-count-b] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-count-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-count-c] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-2-<lambda>1] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-count-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-count-by3] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-count-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-count-by4] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-count-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-mean-a] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-2-<lambda>2] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-2-<lambda>3] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-mean-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-mean-b] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-2-<lambda>4] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-mean-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-mean-c] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-mean-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-mean-by3] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-1-<lambda>0] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-1-<lambda>1] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-1-<lambda>2] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-1-<lambda>3] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-1-<lambda>4] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-mean-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-mean-by4] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-2-<lambda>0] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-mean-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-std-a] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-2-<lambda>1] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-std-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-std-b] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-std-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-std-c] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-2-<lambda>2] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-std-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-std-by3] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-2-<lambda>3] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-std-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-std-by4] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-True-std-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-count-a] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-2-<lambda>4] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-count-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-count-b] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-count-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-count-c] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-count-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-count-by3] 
[gw1] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[size-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-1-<lambda>0] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-count-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-count-by4] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-count-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-mean-a] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-1-<lambda>1] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-1-<lambda>2] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-mean-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-mean-b] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-1-<lambda>3] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-mean-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-mean-c] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-1-<lambda>4] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-mean-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-mean-by3] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-2-<lambda>0] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-mean-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-mean-by4] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-mean-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-std-a] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-std-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-std-b] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-2-<lambda>1] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-std-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-std-c] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-std-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-std-by3] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-std-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-std-by4] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-2-<lambda>2] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[disk-False-std-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-count-a] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-count-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-count-b] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-count-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-count-c] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-count-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-count-by3] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-2-<lambda>3] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-count-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-count-by4] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-count-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-mean-a] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-mean-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-mean-b] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-2-<lambda>4] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-mean-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-mean-c] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-mean-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-mean-by3] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-1-<lambda>0] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-1-<lambda>1] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-mean-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-mean-by4] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-mean-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-std-a] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-1-<lambda>2] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-std-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-std-b] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-std-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-std-c] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-1-<lambda>3] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-std-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-std-by3] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-1-<lambda>4] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-std-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-std-by4] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-True-std-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-count-a] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-count-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-count-b] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-2-<lambda>0] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-count-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-count-c] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-count-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-count-by3] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-count-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-count-by4] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-count-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-mean-a] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-2-<lambda>1] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-mean-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-mean-b] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-2-<lambda>2] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-mean-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-mean-c] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-mean-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-mean-by3] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-2-<lambda>3] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-mean-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-mean-by4] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-mean-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-std-a] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-std-a] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-std-b] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-2-<lambda>4] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-std-b] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-std-c] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-std-c] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-std-by3] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-std-by3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-std-by4] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[std-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-1-<lambda>0] 
[gw0] [ 72%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument[tasks-False-std-by4] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-True-agg0] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-True-agg0] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-True-agg1] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-True-agg1] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-True-agg2] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-True-agg2] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-True-agg3] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-True-agg3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-False-agg0] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-1-<lambda>1] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-False-agg0] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-False-agg1] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-False-agg1] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-False-agg2] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-False-agg2] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-False-agg3] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[disk-False-agg3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-True-agg0] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-True-agg0] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-True-agg1] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-True-agg1] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-True-agg2] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-True-agg2] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-True-agg3] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-True-agg3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-False-agg0] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-False-agg0] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-False-agg1] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-False-agg1] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-False-agg2] 
[gw1] [ 72%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-1-<lambda>1] 
[gw0] [ 72%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-False-agg2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-False-agg3] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_argument_agg[tasks-False-agg3] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_true_split_out[disk] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_true_split_out[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_sort_true_split_out[tasks] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_sort_true_split_out[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-cat_1-ordered-known] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-cat_1-ordered-unknown] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-cat_1-unordererd-known] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-1-<lambda>3] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-cat_1-unordererd-unknown] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-groupby1-ordered-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-groupby1-ordered-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-groupby1-unordererd-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-groupby1-unordererd-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-cat_1-ordered-known] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-cat_1-ordered-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-1-<lambda>4] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-cat_1-unordererd-known] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-groupby1-ordered-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-groupby1-ordered-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-groupby1-unordererd-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-cat_1-ordered-known] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-cat_1-ordered-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-2-<lambda>0] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-cat_1-unordererd-known] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-cat_1-unordererd-unknown] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-groupby1-ordered-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-groupby1-ordered-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-groupby1-unordererd-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-groupby1-unordererd-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-cat_1-ordered-known] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-cat_1-ordered-unknown] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-cat_1-unordererd-known] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-groupby1-ordered-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-groupby1-ordered-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-groupby1-unordererd-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[sum-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-cat_1-ordered-known] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-2-<lambda>1] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-cat_1-ordered-unknown] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-cat_1-unordererd-known] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-2-<lambda>2] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-cat_1-unordererd-unknown] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-groupby1-ordered-known] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-2-<lambda>3] 
[gw0] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-groupby1-ordered-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-2-<lambda>4] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-groupby1-unordererd-known] 
[gw0] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-groupby1-unordererd-unknown] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-cat_1-ordered-known] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-1-<lambda>0] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-cat_1-ordered-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-1-<lambda>1] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-cat_1-unordererd-known] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-1-<lambda>2] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-1-<lambda>3] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-1-<lambda>4] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-groupby1-ordered-known] 
[gw0] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-groupby1-ordered-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-2-<lambda>0] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-groupby1-unordererd-known] 
[gw0] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-2-<lambda>1] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-cat_1-ordered-known] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-cat_1-ordered-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-2-<lambda>2] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-cat_1-unordererd-known] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-cat_1-unordererd-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-2-<lambda>3] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-groupby1-ordered-known] 
[gw0] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-groupby1-ordered-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-2-<lambda>4] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-groupby1-unordererd-known] 
[gw0] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-groupby1-unordererd-unknown] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-cat_1-ordered-known] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[var-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-1-<lambda>0] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-cat_1-ordered-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-1-<lambda>1] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-cat_1-unordererd-known] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-1-<lambda>2] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-1-<lambda>3] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-groupby1-ordered-known] 
[gw0] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-groupby1-ordered-unknown] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-1-<lambda>4] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-groupby1-unordererd-known] 
[gw0] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[mean-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-cat_1-ordered-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-cat_1-ordered-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-cat_1-unordererd-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-cat_1-unordererd-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-groupby1-ordered-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-groupby1-ordered-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-groupby1-unordererd-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-groupby1-unordererd-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-cat_1-ordered-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-cat_1-ordered-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-cat_1-unordererd-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-groupby1-ordered-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-groupby1-ordered-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-groupby1-unordererd-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-cat_1-ordered-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-cat_1-ordered-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-cat_1-unordererd-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-cat_1-unordererd-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-groupby1-ordered-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-groupby1-ordered-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-groupby1-unordererd-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-groupby1-unordererd-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-cat_1-ordered-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-cat_1-ordered-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-cat_1-unordererd-known] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-std] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-var] 
[gw1] [ 73%] XFAIL dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-2-<lambda>0] 
[gw1] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-2-<lambda>0] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false_cov_corr[disk-cov] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-2-<lambda>1] 
[gw1] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-2-<lambda>2] 
[gw1] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-2-<lambda>3] 
[gw1] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-2-<lambda>3] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false_cov_corr[disk-cov] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false_cov_corr[disk-corr] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-2-<lambda>4] 
[gw1] [ 73%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-1-<lambda>0] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false_cov_corr[disk-corr] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false_cov_corr[tasks-cov] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false_cov_corr[tasks-cov] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false_cov_corr[tasks-corr] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false_cov_corr[tasks-corr] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false[disk-cumsum] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false[disk-cumsum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false[disk-cumprod] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false[disk-cumprod] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false[tasks-cumsum] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false[tasks-cumsum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false[tasks-cumprod] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_false[tasks-cumprod] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-True-True-var] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-1-<lambda>1] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-True-True-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-True-True-std] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-True-True-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-True-False-var] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-True-False-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-True-False-std] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-True-False-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-False-True-var] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-False-True-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-False-True-std] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-False-True-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-False-False-var] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-1-<lambda>2] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-False-False-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-False-False-std] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[disk-False-False-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-True-True-var] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-True-True-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-True-True-std] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-True-True-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-True-False-var] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-True-False-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-True-False-std] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-True-False-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-False-True-var] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-False-True-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-False-True-std] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-1-<lambda>3] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-False-True-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-False-False-var] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-False-False-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-False-False-std] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_var_dropna_observed[tasks-False-False-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts_all_na_partitions[disk] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts_all_na_partitions[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_value_counts_all_na_partitions[tasks] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_value_counts_all_na_partitions[tasks] 
dask/dataframe/tests/test_groupby.py::test_agg_pyarrow_casts[disk] 
[gw1] [ 73%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-1-<lambda>4] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_agg_pyarrow_casts[disk] 
dask/dataframe/tests/test_groupby.py::test_agg_pyarrow_casts[tasks] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_groupby.py::test_agg_pyarrow_casts[tasks] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj0] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj0] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj1] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj1] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj2] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj2] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj3] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj3] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj4] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj4] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj5] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj5] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj6] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj6] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj7] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj7] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj8] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj8] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj9] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj9] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj10] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj10] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj11] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj11] 
dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj12] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_pandas_object[obj12] 
dask/dataframe/tests/test_hashing.py::test_categorical_consistency 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_categorical_consistency 
dask/dataframe/tests/test_hashing.py::test_object_missing_values 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_object_missing_values 
dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj0] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj0] 
dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj1] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj1] 
dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj2] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj2] 
dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj3] 
[gw0] [ 73%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj3] 
dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj4] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj4] 
dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj5] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj5] 
dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj6] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hashing.py::test_hash_object_dispatch[obj6] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df0] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df0] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df1] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df1] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df2] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df2] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df3] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df3] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df4] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df4] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df5] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df5] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df6] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df6] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df7] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df7] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df8] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df8] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df9] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df9] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df10] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df10] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df11] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df11] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df12] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[2-df12] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df0] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df0] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df1] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df1] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df2] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df2] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df3] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df3] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df4] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df4] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df5] 
[gw1] [ 74%] XFAIL dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-2-<lambda>0] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-2-<lambda>0] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df5] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df6] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-2-<lambda>1] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-2-<lambda>1] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df6] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df7] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-2-<lambda>2] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-2-<lambda>2] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df7] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df8] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-2-<lambda>3] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-2-<lambda>3] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df8] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df9] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-2-<lambda>4] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[cov-tasks-2-<lambda>4] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df9] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df10] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-1-<lambda>0] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df10] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df11] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df11] 
dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df12] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_basic[20-df12] 
dask/dataframe/tests/test_hyperloglog.py::test_split_every[2-None] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_split_every[2-None] 
dask/dataframe/tests/test_hyperloglog.py::test_split_every[2-2] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_split_every[2-2] 
dask/dataframe/tests/test_hyperloglog.py::test_split_every[2-10] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_split_every[2-10] 
dask/dataframe/tests/test_hyperloglog.py::test_split_every[20-None] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_split_every[20-None] 
dask/dataframe/tests/test_hyperloglog.py::test_split_every[20-2] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_split_every[20-2] 
dask/dataframe/tests/test_hyperloglog.py::test_split_every[20-10] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_split_every[20-10] 
dask/dataframe/tests/test_hyperloglog.py::test_larger_data 
[gw1] [ 74%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-1-<lambda>1] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_hyperloglog.py::test_larger_data 
dask/dataframe/tests/test_indexing.py::test_loc 
[gw1] [ 74%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-1-<lambda>2] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc 
dask/dataframe/tests/test_indexing.py::test_loc_non_informative_index 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_non_informative_index 
dask/dataframe/tests/test_indexing.py::test_loc_with_text_dates 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_with_text_dates 
dask/dataframe/tests/test_indexing.py::test_loc_with_series 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_with_series 
dask/dataframe/tests/test_indexing.py::test_loc_with_array 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_with_array 
dask/dataframe/tests/test_indexing.py::test_loc_with_function 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_with_function 
dask/dataframe/tests/test_indexing.py::test_loc_with_array_different_partition 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_with_array_different_partition 
dask/dataframe/tests/test_indexing.py::test_loc_with_series_different_partition 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_with_series_different_partition 
dask/dataframe/tests/test_indexing.py::test_loc_with_non_boolean_series 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_with_non_boolean_series 
dask/dataframe/tests/test_indexing.py::test_loc2d 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc2d 
dask/dataframe/tests/test_indexing.py::test_loc2d_with_known_divisions 
[gw1] [ 74%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-1-<lambda>3] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc2d_with_known_divisions 
dask/dataframe/tests/test_indexing.py::test_loc2d_with_unknown_divisions 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc2d_with_unknown_divisions 
dask/dataframe/tests/test_indexing.py::test_getitem 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_getitem 
dask/dataframe/tests/test_indexing.py::test_getitem_slice 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_getitem_slice 
dask/dataframe/tests/test_indexing.py::test_getitem_integer_slice 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_getitem_integer_slice 
dask/dataframe/tests/test_indexing.py::test_loc_on_numpy_datetimes 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_on_numpy_datetimes 
dask/dataframe/tests/test_indexing.py::test_loc_on_pandas_datetimes 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_on_pandas_datetimes 
dask/dataframe/tests/test_indexing.py::test_loc_datetime_no_freq 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_datetime_no_freq 
dask/dataframe/tests/test_indexing.py::test_coerce_loc_index 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_coerce_loc_index 
dask/dataframe/tests/test_indexing.py::test_loc_timestamp_str 
[gw1] [ 74%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-1-<lambda>4] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_timestamp_str 
dask/dataframe/tests/test_indexing.py::test_getitem_timestamp_str 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_getitem_timestamp_str 
dask/dataframe/tests/test_indexing.py::test_loc_period_str 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_loc_period_str 
dask/dataframe/tests/test_indexing.py::test_getitem_period_str 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_getitem_period_str 
dask/dataframe/tests/test_indexing.py::test_to_series[index0] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_to_series[index0] 
dask/dataframe/tests/test_indexing.py::test_to_series[index1] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_to_series[index1] 
dask/dataframe/tests/test_indexing.py::test_to_frame[index0] 
[gw1] [ 74%] XFAIL dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-2-<lambda>0] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-2-<lambda>0] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_to_frame[index0] 
dask/dataframe/tests/test_indexing.py::test_to_frame[index1] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_to_frame[index1] 
dask/dataframe/tests/test_indexing.py::test_iloc[0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-2-<lambda>1] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-2-<lambda>1] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_iloc[0] 
dask/dataframe/tests/test_indexing.py::test_iloc[indexer1] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_iloc[indexer1] 
dask/dataframe/tests/test_indexing.py::test_iloc[indexer2] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_iloc[indexer2] 
dask/dataframe/tests/test_indexing.py::test_iloc[indexer3] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_iloc[indexer3] 
dask/dataframe/tests/test_indexing.py::test_iloc[indexer4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-2-<lambda>2] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-2-<lambda>2] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_iloc[indexer4] 
dask/dataframe/tests/test_indexing.py::test_iloc_series 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_iloc_series 
dask/dataframe/tests/test_indexing.py::test_iloc_raises 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_iloc_raises 
dask/dataframe/tests/test_indexing.py::test_iloc_duplicate_columns 
[gw0] [ 74%] XFAIL dask/dataframe/tests/test_indexing.py::test_iloc_duplicate_columns 
dask/dataframe/tests/test_indexing.py::test_iloc_dispatch_to_getitem 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-2-<lambda>3] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-2-<lambda>3] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_iloc_dispatch_to_getitem 
dask/dataframe/tests/test_indexing.py::test_iloc_out_of_order_selection 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_iloc_out_of_order_selection 
dask/dataframe/tests/test_indexing.py::test_pandas_nullable_boolean_data_type 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-2-<lambda>4] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-disk-2-<lambda>4] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_pandas_nullable_boolean_data_type 
dask/dataframe/tests/test_indexing.py::test_deterministic_hashing_series 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_indexing.py::test_deterministic_hashing_series 
dask/dataframe/tests/test_indexing.py::test_gpu_loc 
[gw0] [ 74%] SKIPPED dask/dataframe/tests/test_indexing.py::test_gpu_loc 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-inner-disk] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-1-<lambda>0] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-inner-tasks] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-left-disk] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-left-tasks] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-right-disk] 
[gw1] [ 74%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-1-<lambda>0] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-right-disk] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-1-<lambda>1] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-right-tasks] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-outer-disk] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-outer-tasks] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[idx-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-inner-disk] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-inner-tasks] 
[gw1] [ 74%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-1-<lambda>2] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-left-disk] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-left-tasks] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-right-disk] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-right-tasks] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-outer-disk] 
[gw1] [ 74%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-1-<lambda>3] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-outer-tasks] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on1-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-inner-disk] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-inner-tasks] 
[gw1] [ 74%] XPASS dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-1-<lambda>4] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-left-disk] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-left-tasks] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-right-disk] 
[gw1] [ 74%] XFAIL dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-2-<lambda>0] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-2-<lambda>1] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-2-<lambda>2] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-2-<lambda>2] 
[gw0] [ 74%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-right-tasks] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-2-<lambda>3] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-2-<lambda>4] 
[gw1] [ 74%] SKIPPED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[corr-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-1-<lambda>0] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-1-<lambda>1] 
[gw0] [ 74%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-outer-disk] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-1-<lambda>2] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-1-<lambda>3] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-1-<lambda>4] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-2-<lambda>0] 
[gw0] [ 74%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-outer-tasks] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-2-<lambda>1] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-2-<lambda>2] 
[gw0] [ 74%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-inner-disk] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-2-<lambda>3] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-inner-tasks] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-left-disk] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-2-<lambda>4] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-left-tasks] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-1-<lambda>0] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-1-<lambda>1] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-right-disk] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-1-<lambda>2] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-1-<lambda>3] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-1-<lambda>4] 
[gw0] [ 74%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-right-tasks] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-2-<lambda>0] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-2-<lambda>1] 
[gw0] [ 74%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-outer-disk] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-2-<lambda>2] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-2-<lambda>3] 
[gw0] [ 74%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-outer-tasks] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-2-<lambda>4] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[nunique-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-1-<lambda>0] 
[gw0] [ 74%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[idx-disk-inner] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-1-<lambda>1] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[idx-disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[idx-disk-left] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[idx-disk-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[idx-tasks-inner] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-1-<lambda>2] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[idx-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[idx-tasks-left] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[idx-tasks-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on1-disk-inner] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-1-<lambda>3] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on1-disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on1-disk-left] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on1-disk-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on1-tasks-inner] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-1-<lambda>4] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on1-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on1-tasks-left] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on1-tasks-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on2-disk-inner] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-2-<lambda>0] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on2-disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on2-disk-left] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on2-disk-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on2-tasks-inner] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on2-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on2-tasks-left] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on2-tasks-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on3-disk-inner] 
[gw1] [ 74%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-2-<lambda>1] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on3-disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on3-disk-left] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on3-disk-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on3-tasks-inner] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on3-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on3-tasks-left] 
[gw0] [ 74%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_single[on3-tasks-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[idx-disk-inner] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-2-<lambda>2] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[idx-disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[idx-disk-right] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[idx-disk-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[idx-tasks-inner] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[idx-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[idx-tasks-right] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[idx-tasks-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on1-disk-inner] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-2-<lambda>3] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on1-disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on1-disk-right] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on1-disk-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on1-tasks-inner] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on1-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on1-tasks-right] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on1-tasks-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on2-disk-inner] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on2-disk-inner] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-2-<lambda>3] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on2-disk-right] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-2-<lambda>4] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on2-disk-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on2-tasks-inner] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on2-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on2-tasks-right] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-1-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-1-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on2-tasks-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on3-disk-inner] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-1-<lambda>2] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on3-disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on3-disk-right] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-1-<lambda>3] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-1-<lambda>4] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on3-disk-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on3-tasks-inner] 
[gw0] [ 75%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on3-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on3-tasks-right] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-2-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on3-tasks-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-inner-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-2-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-inner-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-2-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-2-<lambda>3] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-left-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-2-<lambda>4] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-left-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[first-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-1-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-1-<lambda>1] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-1-<lambda>2] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-right-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-1-<lambda>3] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-1-<lambda>4] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-2-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-right-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-2-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-outer-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-2-<lambda>2] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-outer-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-2-<lambda>3] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-inner-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-2-<lambda>4] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-inner-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-1-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-1-<lambda>1] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-1-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-1-<lambda>3] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-left-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-1-<lambda>4] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-2-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-left-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-2-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-right-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-2-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-2-<lambda>3] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-right-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-2-<lambda>4] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-outer-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[last-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-1-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-1-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-outer-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-1-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-1-<lambda>3] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-1-<lambda>4] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-inner-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-1-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-2-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-2-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-inner-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-2-<lambda>2] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-left-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-2-<lambda>3] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-left-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-2-<lambda>4] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-disk-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-1-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-right-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-1-<lambda>1] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-1-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-1-<lambda>3] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-right-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-1-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-1-<lambda>4] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-1-<lambda>4] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-outer-disk] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-2-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-2-<lambda>1] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-2-<lambda>2] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-outer-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-2-<lambda>3] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-inner-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-2-<lambda>3] 
dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-2-<lambda>4] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-inner-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[prod-tasks-2-<lambda>4] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-1-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-1-<lambda>1] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-1-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-2-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-left-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-2-<lambda>1] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-2-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-1-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-1-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-left-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-1-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-2-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-2-<lambda>1] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-2-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[sum-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-1-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-right-disk] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-1-<lambda>1] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-1-<lambda>2] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-2-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-right-tasks] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-2-<lambda>1] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-2-<lambda>2] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-outer-disk] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-1-<lambda>0] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-1-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-outer-tasks] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-1-<lambda>2] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-2-<lambda>0] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-2-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-inner-disk] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-2-<lambda>2] 
[gw1] [ 75%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[mean-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-1-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-inner-tasks] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-left-disk] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-1-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-left-tasks] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-1-<lambda>2] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-right-disk] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-2-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-right-tasks] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-outer-disk] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-2-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-outer-tasks] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-2-<lambda>2] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-inner-disk] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-1-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-inner-tasks] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-left-disk] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-1-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-left-tasks] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-1-<lambda>2] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-right-disk] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-right-tasks] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-2-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-outer-disk] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-2-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-outer-tasks] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-2-<lambda>2] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-inner-disk] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-inner-tasks] 
[gw1] [ 75%] FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-1-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-1-<lambda>1] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-1-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-2-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-2-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-left-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-2-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-1-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-1-<lambda>1] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-1-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-2-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-2-<lambda>1] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-left-tasks] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-2-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[min-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-1-<lambda>0] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-1-<lambda>1] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-1-<lambda>2] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-2-<lambda>0] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-right-disk] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-2-<lambda>1] 
[gw1] [ 75%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-2-<lambda>2] 
[gw0] [ 75%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-right-tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-1-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-2-<lambda>0] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-outer-disk] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-2-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-outer-tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[max-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-1-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-2-<lambda>0] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-inner-disk] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-2-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-1-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-2-<lambda>0] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-inner-tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-2-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[count-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-1-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-1-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-inner-tasks] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-2-<lambda>0] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-left-disk] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-2-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-1-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-1-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-left-tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-2-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-2-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[size-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-1-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-1-<lambda>1] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-right-disk] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-1-<lambda>2] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-2-<lambda>0] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-right-tasks] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-2-<lambda>1] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-outer-disk] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-2-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-outer-tasks] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-1-<lambda>0] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-inner-disk] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-1-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-1-<lambda>2] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-2-<lambda>0] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-inner-tasks] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-2-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-2-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-left-disk] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[std-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-1-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-1-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-1-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-left-tasks] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-2-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-2-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-2-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-right-disk] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-1-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-1-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-1-<lambda>2] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-1-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-right-tasks] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-2-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-2-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-2-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-outer-disk] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[var-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-1-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-1-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-1-<lambda>2] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-2-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-2-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-2-<lambda>2] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-1-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-1-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-1-<lambda>2] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-2-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-2-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-2-<lambda>2] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[cov-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-1-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-1-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-1-<lambda>2] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-2-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-2-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-2-<lambda>2] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-1-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-1-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-1-<lambda>2] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-2-<lambda>0] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-2-<lambda>1] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-2-<lambda>2] 
[gw1] [ 76%] XPASS dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[corr-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-1-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-2-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-2-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-outer-tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-1-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-2-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-2-<lambda>1] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-inner-disk] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-2-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[nunique-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-1-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-1-<lambda>1] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-inner-tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-2-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-2-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-1-<lambda>0] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-left-disk] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-2-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-2-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[first-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-1-<lambda>0] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-left-tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-2-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-2-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-right-disk] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-1-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-2-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-2-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-right-tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[last-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-1-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-2-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-2-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-outer-disk] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-disk-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-1-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-1-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-1-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-1-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-1-<lambda>2] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-1-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-2-<lambda>0] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-2-<lambda>0] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-2-<lambda>1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-2-<lambda>1] 
dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-2-<lambda>2] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-outer-tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[prod-tasks-2-<lambda>2] 
dask/dataframe/tests/test_groupby.py::test_groupy_non_aligned_index[disk] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_groupy_non_aligned_index[disk] 
dask/dataframe/tests/test_groupby.py::test_groupy_non_aligned_index[tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_groupy_non_aligned_index[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupy_series_wrong_grouper[disk] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_groupy_series_wrong_grouper[disk] 
dask/dataframe/tests/test_groupby.py::test_groupy_series_wrong_grouper[tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_groupy_series_wrong_grouper[tasks] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-2-1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-2-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-2-4] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-2-4] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-2-20] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-2-20] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-5-1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-5-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-5-4] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-5-4] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-5-20] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-inner-disk] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-1-5-20] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-2-1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-2-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-2-4] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-2-4] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-2-20] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-inner-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-inner-tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-2-20] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-5-1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-5-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-5-4] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-5-4] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-5-20] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-left-disk] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-5-5-20] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-2-1] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-2-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-2-4] 
[gw0] [ 76%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-left-tasks] 
[gw1] [ 76%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-2-4] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-2-20] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-right-disk] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-2-20] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-5-1] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-5-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-5-4] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-right-tasks] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-5-4] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-5-20] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-outer-disk] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-outer-tasks] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[disk-20-5-20] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-2-1] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-2-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-2-4] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-2-4] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-2-20] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-2-20] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-5-1] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-inner-disk] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-5-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-5-4] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-5-4] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-5-20] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-1-5-20] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-2-1] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-2-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-2-4] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-inner-disk] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-2-4] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-inner-tasks] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-2-20] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-2-20] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-5-1] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-5-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-5-4] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-inner-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-left-disk] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-5-4] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-5-20] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-5-5-20] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-2-1] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-left-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-left-tasks] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-2-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-2-4] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-2-4] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-2-20] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-left-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-right-disk] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-2-20] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-5-1] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-right-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-right-tasks] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-5-1] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-5-4] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-right-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-outer-disk] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-5-4] 
dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-5-20] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-outer-disk] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-outer-tasks] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_hash_groupby_aggregate[tasks-20-5-20] 
dask/dataframe/tests/test_groupby.py::test_split_out_multi_column_groupby[disk] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_split_out_multi_column_groupby[disk] 
dask/dataframe/tests/test_groupby.py::test_split_out_multi_column_groupby[tasks] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-outer-tasks] 
dask/dataframe/tests/test_merge_column_and_index.py::test_join[tasks-left] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_split_out_multi_column_groupby[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_num[disk] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_join[tasks-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_join[tasks-right] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_num[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_split_out_num[tasks] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_num[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_not_supported[disk] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_not_supported[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_not_supported[tasks] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_not_supported[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_column[disk] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_column[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_column[tasks] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_column[tasks] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-a] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_join[tasks-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_join[tasks-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_join[tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_join[tasks-outer] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-c] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_join[tasks-outer] 
dask/dataframe/tests/test_merge_column_and_index.py::test_join[disk-left] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_join[disk-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_join[disk-right] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-d] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_join[disk-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_join[disk-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_join[disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_join[disk-outer] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-d] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-sel3] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_join[disk-outer] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[idx-disk-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[idx-disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[idx-disk-left] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-sel4] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[idx-disk-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[idx-tasks-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[idx-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[idx-tasks-left] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[idx-tasks-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on1-disk-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on1-disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on1-disk-left] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on1-disk-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on1-tasks-inner] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-sel4] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-a] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on1-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on1-tasks-left] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on1-tasks-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on2-disk-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on2-disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on2-disk-left] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on2-disk-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on2-tasks-inner] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-c] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on2-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on2-tasks-left] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on2-tasks-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on3-disk-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on3-disk-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on3-disk-left] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on3-disk-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on3-tasks-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on3-tasks-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on3-tasks-left] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-d] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_right[on3-tasks-left] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-disk-True-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-disk-True-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-disk-True-right] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-disk-True-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-disk-0.75-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-disk-0.75-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-disk-0.75-right] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-d] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-sel3] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-disk-0.75-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-tasks-True-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-tasks-True-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-tasks-True-right] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-tasks-True-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-tasks-0.75-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-tasks-0.75-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-tasks-0.75-right] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[idx-tasks-0.75-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-disk-True-inner] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-sel4] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-disk-True-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-disk-True-right] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-disk-True-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-disk-0.75-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-disk-0.75-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-disk-0.75-right] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-disk-0.75-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-tasks-True-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-tasks-True-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-tasks-True-right] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-tasks-True-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-tasks-0.75-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-tasks-0.75-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-tasks-0.75-right] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-sel4] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-a] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on1-tasks-0.75-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-disk-True-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-disk-True-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-disk-True-right] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-disk-True-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-disk-0.75-inner] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-a] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-c] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-disk-0.75-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-disk-0.75-right] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-disk-0.75-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-tasks-True-inner] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-c] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-d] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-tasks-True-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-tasks-True-right] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-tasks-True-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-tasks-0.75-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-tasks-0.75-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-tasks-0.75-right] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-d] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-sel3] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-tasks-0.75-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-disk-True-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-disk-True-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-disk-True-right] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-sel3] 
dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-sel4] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-disk-True-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-disk-0.75-inner] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-disk-0.75-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-disk-0.75-right] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-disk-0.75-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-tasks-True-inner] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-sel4] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-groupby1-ordered-known] 
[gw1] [ 77%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-groupby1-ordered-unknown] 
[gw1] [ 77%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-groupby1-unordererd-known] 
[gw1] [ 77%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 77%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[median-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-ordered-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-ordered-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-tasks-True-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-tasks-True-right] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-tasks-True-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-tasks-0.75-inner] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-unordererd-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-unordererd-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-tasks-0.75-inner] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-tasks-0.75-right] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-tasks-0.75-right] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_column_with_nulls[None] 
[gw0] [ 77%] SKIPPED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_column_with_nulls[None] 
dask/dataframe/tests/test_merge_column_and_index.py::test_merge_column_with_nulls[4] 
[gw0] [ 77%] SKIPPED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_column_with_nulls[4] 
dask/dataframe/tests/test_methods.py::test_assign_not_modifying_array_inplace 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-ordered-known] 
[gw0] [ 77%] FAILED dask/dataframe/tests/test_methods.py::test_assign_not_modifying_array_inplace 
dask/dataframe/tests/test_multi.py::test_merge_indexed_dataframe_to_indexed_dataframe 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-ordered-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_indexed_dataframe_to_indexed_dataframe 
dask/dataframe/tests/test_multi.py::test_sequential_joins 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_sequential_joins 
dask/dataframe/tests/test_multi.py::test_merge_asof_indexed 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_indexed 
dask/dataframe/tests/test_multi.py::test_merge_asof_on_basic 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-unordererd-known] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on_basic 
dask/dataframe/tests/test_multi.py::test_merge_asof_on_lefton_righton_error 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on_lefton_righton_error 
dask/dataframe/tests/test_multi.py::test_merge_asof_by_leftby_rightby_error 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_by_leftby_rightby_error 
dask/dataframe/tests/test_multi.py::test_merge_asof_on[backward-True] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-unordererd-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on[backward-True] 
dask/dataframe/tests/test_multi.py::test_merge_asof_on[backward-False] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on[backward-False] 
dask/dataframe/tests/test_multi.py::test_merge_asof_on[forward-True] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on[forward-True] 
dask/dataframe/tests/test_multi.py::test_merge_asof_on[forward-False] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on[forward-False] 
dask/dataframe/tests/test_multi.py::test_merge_asof_on[nearest-True] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on[nearest-True] 
dask/dataframe/tests/test_multi.py::test_merge_asof_on[nearest-False] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-ordered-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-ordered-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on[nearest-False] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-backward-True] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-unordererd-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-backward-True] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-backward-False] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-ordered-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-ordered-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-backward-False] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-forward-True] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-unordererd-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-forward-True] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-forward-False] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-ordered-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-ordered-unknown] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-unordererd-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-forward-False] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-nearest-True] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-ordered-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-ordered-unknown] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-unordererd-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-unordererd-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-nearest-True] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-nearest-False] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-ordered-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-ordered-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[False-nearest-False] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-backward-True] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-unordererd-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-backward-True] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-backward-False] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-ordered-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-ordered-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-backward-False] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-forward-True] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-unordererd-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-forward-True] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-forward-False] 
[gw1] [ 77%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-ordered-known] 
[gw1] [ 77%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-ordered-unknown] 
[gw0] [ 77%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-forward-False] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-nearest-True] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-unordererd-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-unordererd-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-nearest-True] 
dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-nearest-False] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-ordered-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-ordered-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_left_on_right_index[True-nearest-False] 
dask/dataframe/tests/test_multi.py::test_merge_asof_indexed_two_partitions 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-unordererd-known] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_indexed_two_partitions 
dask/dataframe/tests/test_multi.py::test_merge_asof_on_by 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-unordererd-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on_by 
dask/dataframe/tests/test_multi.py::test_merge_asof_on_by_tolerance 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on_by_tolerance 
dask/dataframe/tests/test_multi.py::test_merge_asof_on_by_tolerance_no_exact_matches 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on_by_tolerance_no_exact_matches 
dask/dataframe/tests/test_multi.py::test_merge_asof_unsorted_raises 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_unsorted_raises 
dask/dataframe/tests/test_multi.py::test_merge_asof_with_empty 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-ordered-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-ordered-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge_asof_with_empty 
dask/dataframe/tests/test_multi.py::test_merge_asof_on_left_right[endofweek-timestamp] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on_left_right[endofweek-timestamp] 
dask/dataframe/tests/test_multi.py::test_merge_asof_on_left_right[endofweek-endofweek] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-unordererd-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_on_left_right[endofweek-endofweek] 
dask/dataframe/tests/test_multi.py::test_merge_asof_with_various_npartitions 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_asof_with_various_npartitions 
dask/dataframe/tests/test_multi.py::test_indexed_concat[inner] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_indexed_concat[inner] 
dask/dataframe/tests/test_multi.py::test_indexed_concat[outer] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_indexed_concat[outer] 
dask/dataframe/tests/test_multi.py::test_concat[inner] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-ordered-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-ordered-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_concat[inner] 
dask/dataframe/tests/test_multi.py::test_concat[outer] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_concat[outer] 
dask/dataframe/tests/test_multi.py::test_concat_series[inner] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_concat_series[inner] 
dask/dataframe/tests/test_multi.py::test_concat_series[outer] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-unordererd-known] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_concat_series[outer] 
dask/dataframe/tests/test_multi.py::test_concat_with_operation_remains_hlg 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_concat_with_operation_remains_hlg 
dask/dataframe/tests/test_multi.py::test_concat_dataframe_empty 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_concat_dataframe_empty 
dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-inner] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-ordered-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-ordered-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-inner] 
dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-outer] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-unordererd-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-outer] 
dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-left] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-ordered-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-ordered-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-left] 
dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-right] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-unordererd-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-unordererd-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-right] 
dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[False-inner] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[False-inner] 
dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[False-outer] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[False-outer] 
dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[False-left] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[False-left] 
dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[False-right] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[False-right] 
dask/dataframe/tests/test_multi.py::test_merge[disk-inner] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-ordered-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-ordered-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge[disk-inner] 
dask/dataframe/tests/test_multi.py::test_merge[disk-outer] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-unordererd-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge[disk-outer] 
dask/dataframe/tests/test_multi.py::test_merge[disk-left] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-ordered-known] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge[disk-left] 
dask/dataframe/tests/test_multi.py::test_merge[disk-right] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-ordered-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge[disk-right] 
dask/dataframe/tests/test_multi.py::test_merge[tasks-inner] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-unordererd-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge[tasks-inner] 
dask/dataframe/tests/test_multi.py::test_merge[tasks-outer] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-ordered-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-ordered-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge[tasks-outer] 
dask/dataframe/tests/test_multi.py::test_merge[tasks-left] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge[tasks-left] 
dask/dataframe/tests/test_multi.py::test_merge[tasks-right] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-unordererd-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-unordererd-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge[tasks-right] 
dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[disk-right] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[disk-right] 
dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[disk-outer] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-groupby1-ordered-known] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-groupby1-ordered-unknown] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-groupby1-unordererd-known] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-groupby1-unordererd-unknown] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-ordered-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-ordered-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[disk-outer] 
dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[tasks-right] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-unordererd-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[tasks-right] 
dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[tasks-outer] 
[gw0] [ 78%] FAILED dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[tasks-outer] 
dask/dataframe/tests/test_multi.py::test_merge_how_raises 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_how_raises 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftsemi-parts0] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-groupby1-ordered-known] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-groupby1-ordered-unknown] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-groupby1-unordererd-known] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-ordered-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-ordered-unknown] 
[gw0] [ 78%] XFAIL dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftsemi-parts0] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftsemi-parts1] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-unordererd-unknown] 
[gw0] [ 78%] XFAIL dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftsemi-parts1] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftsemi-parts2] 
[gw0] [ 78%] XFAIL dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftsemi-parts2] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftanti-parts0] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-groupby1-ordered-known] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-groupby1-ordered-unknown] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-groupby1-unordererd-known] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-groupby1-unordererd-unknown] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-ordered-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-ordered-unknown] 
[gw0] [ 78%] XFAIL dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftanti-parts0] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftanti-parts1] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-unordererd-known] 
[gw0] [ 78%] XFAIL dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftanti-parts1] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftanti-parts2] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 78%] XFAIL dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[pandas-leftanti-parts2] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftsemi-parts0] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftsemi-parts0] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftsemi-parts1] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftsemi-parts1] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftsemi-parts2] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftsemi-parts2] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftanti-parts0] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftanti-parts0] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftanti-parts1] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftanti-parts1] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftanti-parts2] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_tasks_semi_anti_cudf[cudf-leftanti-parts2] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_passes_through 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_tasks_passes_through 
dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[disk-inner] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[disk-inner] 
dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[disk-outer] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[disk-outer] 
dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[disk-left] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[disk-left] 
dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[disk-right] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[disk-right] 
dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[tasks-inner] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[tasks-inner] 
dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[tasks-outer] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[tasks-outer] 
dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[tasks-left] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[tasks-left] 
dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[tasks-right] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_index_patterns[tasks-right] 
dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[disk-inner] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[disk-inner] 
dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[disk-outer] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[disk-outer] 
dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[disk-left] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[disk-left] 
dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[disk-right] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[disk-right] 
dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[tasks-inner] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[tasks-inner] 
dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[tasks-outer] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[tasks-outer] 
dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[tasks-left] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[tasks-left] 
dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[tasks-right] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_join_by_index_patterns[tasks-right] 
dask/dataframe/tests/test_multi.py::test_join_gives_proper_divisions 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-groupby1-ordered-known] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-groupby1-ordered-known] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_join_gives_proper_divisions 
dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[disk-inner] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-groupby1-ordered-unknown] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-groupby1-unordererd-known] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[disk-inner] 
dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[disk-outer] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 78%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-ordered-known] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[disk-outer] 
dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[disk-left] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[disk-left] 
dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[disk-right] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[disk-right] 
dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[tasks-inner] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[tasks-inner] 
dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[tasks-outer] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[tasks-outer] 
dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[tasks-left] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[tasks-left] 
dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[tasks-right] 
[gw0] [ 78%] SKIPPED dask/dataframe/tests/test_multi.py::test_merge_by_multiple_columns[tasks-right] 
dask/dataframe/tests/test_multi.py::test_melt[kwargs0] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-ordered-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_melt[kwargs0] 
dask/dataframe/tests/test_multi.py::test_melt[kwargs1] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_melt[kwargs1] 
dask/dataframe/tests/test_multi.py::test_melt[kwargs2] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_melt[kwargs2] 
dask/dataframe/tests/test_multi.py::test_melt[kwargs3] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_melt[kwargs3] 
dask/dataframe/tests/test_multi.py::test_melt[kwargs4] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_melt[kwargs4] 
dask/dataframe/tests/test_multi.py::test_melt[kwargs5] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_melt[kwargs5] 
dask/dataframe/tests/test_multi.py::test_melt[kwargs6] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_melt[kwargs6] 
dask/dataframe/tests/test_multi.py::test_cheap_inner_merge_with_pandas_object 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_cheap_inner_merge_with_pandas_object 
dask/dataframe/tests/test_multi.py::test_cheap_single_partition_merge[False] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_cheap_single_partition_merge[False] 
dask/dataframe/tests/test_multi.py::test_cheap_single_partition_merge[True] 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-unordererd-known] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_cheap_single_partition_merge[True] 
dask/dataframe/tests/test_multi.py::test_cheap_single_partition_merge_divisions 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_cheap_single_partition_merge_divisions 
dask/dataframe/tests/test_multi.py::test_cheap_single_parition_merge_left_right[False-left] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-unordererd-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_cheap_single_parition_merge_left_right[False-left] 
dask/dataframe/tests/test_multi.py::test_cheap_single_parition_merge_left_right[False-right] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_cheap_single_parition_merge_left_right[False-right] 
dask/dataframe/tests/test_multi.py::test_cheap_single_parition_merge_left_right[True-left] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_cheap_single_parition_merge_left_right[True-left] 
dask/dataframe/tests/test_multi.py::test_cheap_single_parition_merge_left_right[True-right] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_cheap_single_parition_merge_left_right[True-right] 
dask/dataframe/tests/test_multi.py::test_cheap_single_partition_merge_on_index 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-ordered-known] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_cheap_single_partition_merge_on_index 
dask/dataframe/tests/test_multi.py::test_merge_maintains_columns 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-ordered-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_maintains_columns 
dask/dataframe/tests/test_multi.py::test_merge_index_without_divisions[disk] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_index_without_divisions[disk] 
dask/dataframe/tests/test_multi.py::test_merge_index_without_divisions[tasks] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_merge_index_without_divisions[tasks] 
dask/dataframe/tests/test_multi.py::test_half_indexed_dataframe_avoids_shuffle 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_half_indexed_dataframe_avoids_shuffle 
dask/dataframe/tests/test_multi.py::test_errors_for_merge_on_frame_columns 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_errors_for_merge_on_frame_columns 
dask/dataframe/tests/test_multi.py::test_concat_one_series 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_concat_one_series 
dask/dataframe/tests/test_multi.py::test_concat_unknown_divisions 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_concat_unknown_divisions 
dask/dataframe/tests/test_multi.py::test_concat_unknown_divisions_errors 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_concat_unknown_divisions_errors 
dask/dataframe/tests/test_multi.py::test_concat3 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-unordererd-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-unordererd-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_concat3 
dask/dataframe/tests/test_multi.py::test_concat4_interleave_partitions 
[gw1] [ 78%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-ordered-known] 
[gw1] [ 78%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-ordered-unknown] 
[gw0] [ 78%] PASSED dask/dataframe/tests/test_multi.py::test_concat4_interleave_partitions 
dask/dataframe/tests/test_multi.py::test_concat5 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-unordererd-known] 
[gw1] [ 79%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-ordered-known] 
[gw1] [ 79%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-ordered-unknown] 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-unordererd-known] 
[gw1] [ 79%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-ordered-known] 
[gw1] [ 79%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-ordered-unknown] 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 79%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-unordererd-unknown] 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-ordered-known] 
[gw1] [ 79%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-ordered-unknown] 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-unordererd-known] 
[gw1] [ 79%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-unordererd-unknown] 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-ordered-known] 
[gw1] [ 79%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-ordered-unknown] 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-unordererd-known] 
[gw1] [ 79%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-ordered-known] 
[gw1] [ 79%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-ordered-unknown] 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-unordererd-known] 
[gw0] [ 79%] PASSED dask/dataframe/tests/test_multi.py::test_concat5 
dask/dataframe/tests/test_multi.py::test_concat_categorical[True-True-False] 
[gw1] [ 79%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 79%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-cat_1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-cat_1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-cat_1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-cat_1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-groupby1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-groupby1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-groupby1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-groupby1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-cat_1-ordered-known] 
[gw0] [ 79%] PASSED dask/dataframe/tests/test_multi.py::test_concat_categorical[True-True-False] 
dask/dataframe/tests/test_multi.py::test_concat_categorical[True-False-True] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-cat_1-ordered-unknown] 
[gw0] [ 79%] XFAIL dask/dataframe/tests/test_multi.py::test_concat_categorical[True-False-True] 
dask/dataframe/tests/test_multi.py::test_concat_categorical[True-False-False] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-cat_1-unordererd-known] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 79%] PASSED dask/dataframe/tests/test_multi.py::test_concat_categorical[True-False-False] 
dask/dataframe/tests/test_multi.py::test_concat_categorical[False-True-False] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_concat_categorical[False-True-False] 
dask/dataframe/tests/test_multi.py::test_concat_categorical[False-False-True] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-groupby1-ordered-known] 
[gw1] [ 79%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-groupby1-ordered-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_concat_categorical[False-False-True] 
dask/dataframe/tests/test_multi.py::test_concat_categorical[False-False-False] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-groupby1-unordererd-known] 
[gw1] [ 79%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_concat_categorical[False-False-False] 
dask/dataframe/tests/test_multi.py::test_concat_categorical_mixed_simple 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_concat_categorical_mixed_simple 
dask/dataframe/tests/test_multi.py::test_concat_datetimeindex 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-cat_1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-cat_1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-cat_1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-groupby1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-groupby1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-groupby1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-groupby1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-cat_1-ordered-known] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_concat_datetimeindex 
dask/dataframe/tests/test_multi.py::test_singleton_divisions 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-cat_1-ordered-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_singleton_divisions 
dask/dataframe/tests/test_multi.py::test_repartition_repeated_divisions 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-cat_1-unordererd-known] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_repartition_repeated_divisions 
dask/dataframe/tests/test_multi.py::test_multi_duplicate_divisions 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_multi_duplicate_divisions 
dask/dataframe/tests/test_multi.py::test_merge_outer_empty 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-groupby1-ordered-known] 
[gw1] [ 79%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-groupby1-ordered-unknown] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-groupby1-unordererd-known] 
[gw0] [ 79%] PASSED dask/dataframe/tests/test_multi.py::test_merge_outer_empty 
dask/dataframe/tests/test_multi.py::test_dtype_equality_warning 
[gw0] [ 79%] PASSED dask/dataframe/tests/test_multi.py::test_dtype_equality_warning 
dask/dataframe/tests/test_multi.py::test_groupby_concat_cudf[pandas] 
[gw0] [ 79%] PASSED dask/dataframe/tests/test_multi.py::test_groupby_concat_cudf[pandas] 
dask/dataframe/tests/test_multi.py::test_groupby_concat_cudf[cudf] 
[gw0] [ 79%] SKIPPED dask/dataframe/tests/test_multi.py::test_groupby_concat_cudf[cudf] 
dask/dataframe/tests/test_multi.py::test_concat_ignore_order[True] 
[gw1] [ 79%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_concat_ignore_order[True] 
dask/dataframe/tests/test_multi.py::test_concat_ignore_order[False] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_concat_ignore_order[False] 
dask/dataframe/tests/test_multi.py::test_nullable_types_merge[Int64] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[std-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-cat_1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-cat_1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-cat_1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-cat_1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-groupby1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-groupby1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-groupby1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-groupby1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-cat_1-ordered-known] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_nullable_types_merge[Int64] 
dask/dataframe/tests/test_multi.py::test_nullable_types_merge[Float64] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_nullable_types_merge[Float64] 
dask/dataframe/tests/test_multi.py::test_nullable_types_merge[int64[pyarrow]] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-cat_1-ordered-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_nullable_types_merge[int64[pyarrow]] 
dask/dataframe/tests/test_multi.py::test_nullable_types_merge[float64[pyarrow]] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_nullable_types_merge[float64[pyarrow]] 
dask/dataframe/tests/test_multi.py::test_categorical_join 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-cat_1-unordererd-known] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_categorical_join 
dask/dataframe/tests/test_multi.py::test_categorical_merge_with_columns_missing_from_left 
[gw0] [ 79%] PASSED dask/dataframe/tests/test_multi.py::test_categorical_merge_with_columns_missing_from_left 
dask/dataframe/tests/test_multi.py::test_categorical_merge_with_merge_column_cat_in_one_and_not_other_upcasts 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_categorical_merge_with_merge_column_cat_in_one_and_not_other_upcasts 
dask/dataframe/tests/test_multi.py::test_categorical_merge_retains_category_dtype 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-groupby1-ordered-known] 
[gw1] [ 79%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-groupby1-ordered-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_categorical_merge_retains_category_dtype 
dask/dataframe/tests/test_multi.py::test_categorical_merge_does_not_raise_setting_with_copy_warning 
[gw0] [ 79%] PASSED dask/dataframe/tests/test_multi.py::test_categorical_merge_does_not_raise_setting_with_copy_warning 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-28-inner] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-groupby1-unordererd-known] 
[gw1] [ 79%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-28-inner] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-28-left] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-cat_1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-cat_1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-cat_1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-groupby1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-groupby1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-groupby1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-groupby1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-observed-groupby1-unordererd-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-28-left] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-28-right] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-cat_1-ordered-known] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-cat_1-ordered-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-28-right] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-32-inner] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-cat_1-unordererd-known] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-32-inner] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-32-left] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-32-left] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-32-right] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-groupby1-ordered-known] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-32-right] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-28-inner] 
[gw1] [ 79%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-groupby1-ordered-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-28-inner] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-28-left] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-groupby1-unordererd-known] 
[gw1] [ 79%] XPASS dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-28-left] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-28-right] 
[gw1] [ 79%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[var-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-cat_1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-cat_1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-cat_1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-cat_1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-groupby1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-groupby1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-groupby1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-groupby1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-cat_1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-cat_1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-cat_1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-groupby1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-groupby1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-groupby1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-cat_1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-cat_1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-cat_1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-groupby1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-groupby1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-groupby1-unordererd-known] 
[gw0] [ 79%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-28-right] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-32-inner] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-groupby1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-cat_1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-cat_1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-cat_1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-groupby1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-groupby1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-groupby1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[cov-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-cat_1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-cat_1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-cat_1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-cat_1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-groupby1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-groupby1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-groupby1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-groupby1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-cat_1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-cat_1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-cat_1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-groupby1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-groupby1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-groupby1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-cat_1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-cat_1-ordered-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-cat_1-unordererd-unknown] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-groupby1-ordered-known] 
[gw1] [ 79%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-groupby1-ordered-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-groupby1-unordererd-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-groupby1-unordererd-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-cat_1-ordered-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-cat_1-ordered-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-cat_1-unordererd-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-groupby1-ordered-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-groupby1-ordered-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-groupby1-unordererd-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[corr-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-cat_1-ordered-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-cat_1-ordered-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-cat_1-unordererd-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-cat_1-unordererd-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-groupby1-ordered-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-groupby1-ordered-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-groupby1-unordererd-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-groupby1-unordererd-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-cat_1-ordered-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-cat_1-ordered-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-cat_1-unordererd-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-groupby1-ordered-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-groupby1-ordered-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-groupby1-unordererd-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-cat_1-ordered-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-cat_1-ordered-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-cat_1-unordererd-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-groupby1-ordered-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-groupby1-ordered-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-groupby1-unordererd-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-groupby1-unordererd-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-cat_1-ordered-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-cat_1-ordered-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-cat_1-unordererd-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-groupby1-ordered-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-groupby1-ordered-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-groupby1-unordererd-known] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 80%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[nunique-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-ordered-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-ordered-unknown] 
[gw0] [ 80%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-32-inner] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-32-left] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-unordererd-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-unordererd-unknown] 
[gw0] [ 80%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-32-left] 
dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-32-right] 
[gw0] [ 80%] FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-32-right] 
dask/dataframe/tests/test_multi.py::test_pairwise_rejects_unsupported_join_types[right] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_rejects_unsupported_join_types[right] 
dask/dataframe/tests/test_multi.py::test_pairwise_rejects_unsupported_join_types[inner] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_rejects_unsupported_join_types[inner] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-1-left] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-1-left] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-1-outer] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-ordered-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-ordered-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-1-outer] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-2-left] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-unordererd-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-unordererd-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-2-left] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-2-outer] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-ordered-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-ordered-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-2-outer] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-3-left] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-unordererd-known] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-3-left] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-3-outer] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-ordered-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-ordered-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[1-3-outer] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-1-left] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-unordererd-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-1-left] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-1-outer] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-ordered-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-ordered-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-1-outer] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-2-left] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-unordererd-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-2-left] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-2-outer] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-ordered-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-ordered-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-2-outer] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-3-left] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-unordererd-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-unordererd-unknown] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-ordered-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-ordered-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-3-left] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-3-outer] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-unordererd-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[2-3-outer] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-1-left] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-ordered-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-ordered-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-1-left] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-1-outer] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-unordererd-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-1-outer] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-2-left] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-ordered-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-ordered-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-2-left] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-2-outer] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-unordererd-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-unordererd-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-2-outer] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-3-left] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-ordered-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-ordered-unknown] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-unordererd-known] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-3-left] 
dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-3-outer] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-unordererd-unknown] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-ordered-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-ordered-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_pairwise_merge_results_in_identical_output_df[3-3-outer] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-1-1] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-1-1] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-1-2] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-1-2] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-1-3] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-1-3] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-2-1] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-2-1] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-2-2] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-2-2] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-2-3] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-unordererd-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-2-3] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-3-1] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-3-1] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-3-2] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-3-2] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-3-3] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[left-3-3] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-1-1] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-1-1] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-1-2] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-1-2] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-1-3] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-1-3] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-2-1] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-ordered-known] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-2-1] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-2-2] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-ordered-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-2-2] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-2-3] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-2-3] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-3-1] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-3-1] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-3-2] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-3-2] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-3-3] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[right-3-3] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-1-1] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-1-1] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-1-2] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-1-2] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-1-3] 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-unordererd-known] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-1-3] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-2-1] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-2-1] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-2-2] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-2-2] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-2-3] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-2-3] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-3-1] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-3-1] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-3-2] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-3-2] 
dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-3-3] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_multi.py::test_ensure_npartitions_properly_set[outer-3-3] 
dask/dataframe/tests/test_numeric.py::test_to_numeric_on_scalars[5_0] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_numeric.py::test_to_numeric_on_scalars[5_0] 
dask/dataframe/tests/test_numeric.py::test_to_numeric_on_scalars[5_1] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_numeric.py::test_to_numeric_on_scalars[5_1] 
dask/dataframe/tests/test_numeric.py::test_to_numeric_on_scalars[5 ] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_numeric.py::test_to_numeric_on_scalars[5 ] 
dask/dataframe/tests/test_numeric.py::test_to_numeric_on_dask_array 
[gw1] [ 80%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-ordered-known] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_numeric.py::test_to_numeric_on_dask_array 
dask/dataframe/tests/test_numeric.py::test_to_numeric_on_dask_array_with_meta 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_numeric.py::test_to_numeric_on_dask_array_with_meta 
dask/dataframe/tests/test_numeric.py::test_to_numeric_on_dask_dataframe_series 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_numeric.py::test_to_numeric_on_dask_dataframe_series 
dask/dataframe/tests/test_numeric.py::test_to_numeric_on_dask_dataframe_series_with_meta 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_numeric.py::test_to_numeric_on_dask_dataframe_series_with_meta 
dask/dataframe/tests/test_numeric.py::test_to_numeric_on_dask_dataframe_dataframe_raises_error 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_numeric.py::test_to_numeric_on_dask_dataframe_dataframe_raises_error 
dask/dataframe/tests/test_numeric.py::test_to_numeric_raises 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_numeric.py::test_to_numeric_raises 
dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[object-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[object-False] 
[gw1] [ 80%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[str-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[str-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype2-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype2-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype3-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype3-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype4-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype4-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype5-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype5-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype6-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype6-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype7-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype7-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype8-True] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype8-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype9-True] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_pyarrow_string_dtype[dtype9-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[object-True] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[object-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[str-True] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[str-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype2-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype2-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype3-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype3-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype4-True] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype4-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype5-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype5-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype6-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype6-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype7-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype7-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype8-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype8-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype9-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_dtype[dtype9-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index0-True] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index0-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index1-True] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index1-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index2-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index2-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index3-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index3-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index4-False] 
[gw0] [ 80%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index4-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index5-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index5-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index6-True] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index6-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index7-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index7-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index8-True] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index8-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index9-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_index[index9-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series0-True] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series0-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series1-True] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series1-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series2-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series2-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series3-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series3-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series4-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series4-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series5-True] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series5-True] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series6-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series6-False] 
dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series7-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::test_is_object_string_series[series7-False] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series0-True] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series0-True] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series1-True] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series1-True] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series2-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series2-False] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series3-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series3-False] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series4-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series4-False] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series5-True] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series5-True] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series6-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series6-False] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series7-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series7-False] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series8-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series8-False] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series9-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series9-False] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series10-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series10-False] 
dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series11-False] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow.py::tests_is_object_string_dataframe[series11-False] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[uint8] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[uint8] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[uint16] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[uint16] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[uint32] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[uint32] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[uint64] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[uint64] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[int8] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[int8] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[int16] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[int16] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[int32] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[int32] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[int64] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[int64] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[float] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[float] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[double] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[double] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[decimal128(7, 3)] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[decimal128(7, 3)] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[string] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[string] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[binary] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[binary] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[time32[s]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[time32[s]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[time32[ms]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[time32[ms]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[time64[us]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[time64[us]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[time64[ns]] 
[gw1] [ 81%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-unordererd-unknown] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[time64[ns]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[date32[day]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[date32[day]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[date64[ms]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[date64[ms]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[s]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[s]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[s, tz=UTC]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[s, tz=UTC]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[s, tz=US/Pacific]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[s, tz=US/Pacific]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[s, tz=US/Eastern]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[s, tz=US/Eastern]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ms]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ms]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ms, tz=UTC]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ms, tz=UTC]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ms, tz=US/Pacific]] 
[gw1] [ 81%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-ordered-known] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-ordered-unknown] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ms, tz=US/Pacific]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ms, tz=US/Eastern]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ms, tz=US/Eastern]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[us]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[us]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[us, tz=UTC]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[us, tz=UTC]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[us, tz=US/Pacific]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[us, tz=US/Pacific]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[us, tz=US/Eastern]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[us, tz=US/Eastern]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ns]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ns]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ns, tz=UTC]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ns, tz=UTC]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ns, tz=US/Pacific]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ns, tz=US/Pacific]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ns, tz=US/Eastern]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[timestamp[ns, tz=US/Eastern]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[duration[s]] 
[gw1] [ 81%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-unordererd-known] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[duration[s]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[duration[ms]] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-unordererd-unknown] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[duration[ms]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[duration[us]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[duration[us]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[duration[ns]] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[duration[ns]] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[bool] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip[bool] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip_pyarrow_string_implementations[stringdtype] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip_pyarrow_string_implementations[stringdtype] 
dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip_pyarrow_string_implementations[arrowdtype] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_pickle_roundtrip_pyarrow_string_implementations[arrowdtype] 
[gw1] [ 81%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_pyarrow_compat.py::test_inplace_modification_read_only 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-ordered-known] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_pyarrow_compat.py::test_inplace_modification_read_only 
dask/dataframe/tests/test_reshape.py::test_get_dummies[data0] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_get_dummies[data0] 
dask/dataframe/tests/test_reshape.py::test_get_dummies[data1] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-ordered-unknown] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_get_dummies[data1] 
dask/dataframe/tests/test_reshape.py::test_get_dummies[data2] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_get_dummies[data2] 
dask/dataframe/tests/test_reshape.py::test_get_dummies_categories_order 
[gw0] [ 81%] FAILED dask/dataframe/tests/test_reshape.py::test_get_dummies_categories_order 
dask/dataframe/tests/test_reshape.py::test_get_dummies_object 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_get_dummies_object 
dask/dataframe/tests/test_reshape.py::test_get_dummies_kwargs 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_get_dummies_kwargs 
dask/dataframe/tests/test_reshape.py::test_get_dummies_sparse 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_get_dummies_sparse 
dask/dataframe/tests/test_reshape.py::test_get_dummies_sparse_mix 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_get_dummies_sparse_mix 
dask/dataframe/tests/test_reshape.py::test_get_dummies_dtype 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_get_dummies_dtype 
dask/dataframe/tests/test_reshape.py::test_get_dummies_errors 
[gw1] [ 81%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-unordererd-known] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-unordererd-unknown] 
[gw0] [ 81%] FAILED dask/dataframe/tests/test_reshape.py::test_get_dummies_errors 
dask/dataframe/tests/test_reshape.py::test_pivot_table[mean-B] 
[gw1] [ 81%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-ordered-known] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-ordered-unknown] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[mean-B] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[mean-values1] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[mean-values1] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[mean-values2] 
[gw1] [ 81%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-unordererd-known] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-unordererd-unknown] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[mean-values2] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[sum-B] 
[gw1] [ 81%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-cat_1-ordered-known] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-cat_1-ordered-unknown] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[sum-B] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[sum-values1] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-cat_1-unordererd-known] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-cat_1-unordererd-unknown] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[sum-values1] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[sum-values2] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-groupby1-ordered-known] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-groupby1-ordered-unknown] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-groupby1-unordererd-known] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-groupby1-unordererd-unknown] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-observed-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-cat_1-ordered-known] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-cat_1-ordered-unknown] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-cat_1-unordererd-known] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[sum-values2] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[count-B] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-groupby1-ordered-known] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-groupby1-ordered-unknown] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-groupby1-unordererd-known] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-disk-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-cat_1-ordered-known] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-cat_1-ordered-unknown] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[count-B] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[count-values1] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-cat_1-unordererd-known] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-cat_1-unordererd-unknown] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-groupby1-ordered-known] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-groupby1-ordered-unknown] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-groupby1-unordererd-known] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-groupby1-unordererd-unknown] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-observed-groupby1-unordererd-unknown] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[count-values1] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[count-values2] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-cat_1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-cat_1-ordered-unknown] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-cat_1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-cat_1-unordererd-known] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[count-values2] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[first-B] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-cat_1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-cat_1-unordererd-unknown] 
[gw1] [ 81%] XFAIL dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-cat_1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-groupby1-ordered-known] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-groupby1-ordered-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-groupby1-ordered-unknown] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-groupby1-ordered-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-groupby1-unordererd-known] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-groupby1-unordererd-known] 
dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-groupby1-unordererd-unknown] 
[gw1] [ 81%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[prod-tasks-unobserved-groupby1-unordererd-unknown] 
dask/dataframe/tests/test_groupby.py::test_groupby_cov_non_numeric_grouping_column[disk] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_cov_non_numeric_grouping_column[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_cov_non_numeric_grouping_column[tasks] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[first-B] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[first-values1] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_cov_non_numeric_grouping_column[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_None_column_name[disk] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_None_column_name[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_None_column_name[tasks] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_None_column_name[tasks] 
dask/dataframe/tests/test_groupby.py::test_dataframe_named_agg[True] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_named_agg[True] 
dask/dataframe/tests/test_groupby.py::test_dataframe_named_agg[False] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_dataframe_named_agg[False] 
dask/dataframe/tests/test_groupby.py::test_series_named_agg[count-True] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[first-values1] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[first-values2] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_series_named_agg[count-True] 
dask/dataframe/tests/test_groupby.py::test_series_named_agg[count-False] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_series_named_agg[count-False] 
dask/dataframe/tests/test_groupby.py::test_series_named_agg[mean-True] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_series_named_agg[mean-True] 
dask/dataframe/tests/test_groupby.py::test_series_named_agg[mean-False] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_series_named_agg[mean-False] 
dask/dataframe/tests/test_groupby.py::test_series_named_agg[agg2-True] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[first-values2] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[last-B] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_series_named_agg[agg2-True] 
dask/dataframe/tests/test_groupby.py::test_series_named_agg[agg2-False] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_series_named_agg[agg2-False] 
dask/dataframe/tests/test_groupby.py::test_empty_partitions_with_value_counts[disk-A] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_empty_partitions_with_value_counts[disk-A] 
dask/dataframe/tests/test_groupby.py::test_empty_partitions_with_value_counts[disk-by1] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[last-B] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[last-values1] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_empty_partitions_with_value_counts[disk-by1] 
dask/dataframe/tests/test_groupby.py::test_empty_partitions_with_value_counts[tasks-A] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_empty_partitions_with_value_counts[tasks-A] 
dask/dataframe/tests/test_groupby.py::test_empty_partitions_with_value_counts[tasks-by1] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_empty_partitions_with_value_counts[tasks-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_with_pd_grouper[disk] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_with_pd_grouper[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_with_pd_grouper[tasks] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_with_pd_grouper[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_empty_partitions_with_rows_operation[disk-head] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_empty_partitions_with_rows_operation[disk-head] 
dask/dataframe/tests/test_groupby.py::test_groupby_empty_partitions_with_rows_operation[disk-tail] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[last-values1] 
dask/dataframe/tests/test_reshape.py::test_pivot_table[last-values2] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_empty_partitions_with_rows_operation[disk-tail] 
dask/dataframe/tests/test_groupby.py::test_groupby_empty_partitions_with_rows_operation[tasks-head] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_empty_partitions_with_rows_operation[tasks-head] 
dask/dataframe/tests/test_groupby.py::test_groupby_empty_partitions_with_rows_operation[tasks-tail] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_empty_partitions_with_rows_operation[tasks-tail] 
dask/dataframe/tests/test_groupby.py::test_groupby_with_row_operations[disk-head] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_with_row_operations[disk-head] 
dask/dataframe/tests/test_groupby.py::test_groupby_with_row_operations[disk-tail] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_with_row_operations[disk-tail] 
dask/dataframe/tests/test_groupby.py::test_groupby_with_row_operations[tasks-head] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table[last-values2] 
dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[first-B] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_with_row_operations[tasks-head] 
dask/dataframe/tests/test_groupby.py::test_groupby_with_row_operations[tasks-tail] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_with_row_operations[tasks-tail] 
dask/dataframe/tests/test_groupby.py::test_groupby_multi_index_with_row_operations[disk-head] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multi_index_with_row_operations[disk-head] 
dask/dataframe/tests/test_groupby.py::test_groupby_multi_index_with_row_operations[disk-tail] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[first-B] 
dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[first-values1] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multi_index_with_row_operations[disk-tail] 
dask/dataframe/tests/test_groupby.py::test_groupby_multi_index_with_row_operations[tasks-head] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multi_index_with_row_operations[tasks-head] 
dask/dataframe/tests/test_groupby.py::test_groupby_multi_index_with_row_operations[tasks-tail] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_multi_index_with_row_operations[tasks-tail] 
dask/dataframe/tests/test_groupby.py::test_groupby_iter_fails[disk] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_iter_fails[disk] 
dask/dataframe/tests/test_groupby.py::test_groupby_iter_fails[tasks] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_iter_fails[tasks] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-3-key1] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-3-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-3-by1] 
[gw0] [ 81%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[first-values1] 
dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[first-values2] 
[gw1] [ 81%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-3-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-value-key1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-value-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-value-by1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-value-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key2-key1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key2-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key2-by1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key2-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key3-key1] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[first-values2] 
dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[last-B] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key3-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key3-by1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key3-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key4-key1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key4-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key4-by1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key4-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key5-key1] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[last-B] 
dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[last-values1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key5-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key5-by1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[disk-slice_key5-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-3-key1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-3-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-3-by1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-3-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-value-key1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-value-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-value-by1] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[last-values1] 
dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[last-values2] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-value-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key2-key1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key2-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key2-by1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key2-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key3-key1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key3-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key3-by1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key3-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key4-key1] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table_firstlast[last-values2] 
dask/dataframe/tests/test_reshape.py::test_pivot_table_dtype 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key4-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key4-by1] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table_dtype 
dask/dataframe/tests/test_reshape.py::test_pivot_table_index_dtype 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_reshape.py::test_pivot_table_index_dtype 
dask/dataframe/tests/test_reshape.py::test_pivot_table_errors 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key4-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key5-key1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key5-key1] 
dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key5-by1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_slice_getitem[tasks-slice_key5-by1] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-min] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-min] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-max] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-max] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-sum] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-sum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-prod] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-prod] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-first] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-first] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-last] 
[gw0] [ 82%] FAILED dask/dataframe/tests/test_reshape.py::test_pivot_table_errors 
dask/dataframe/tests/test_rolling.py::test_map_overlap[True-1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-last] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-median] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-median] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-idxmax] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-idxmax] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-idxmin] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-None-idxmin] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-min] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-min] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-max] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-max] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-sum] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap[True-1] 
dask/dataframe/tests/test_rolling.py::test_map_overlap[True-4] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-sum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-prod] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-prod] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-first] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-first] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-last] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-last] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-median] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-median] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-idxmax] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-idxmax] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-idxmin] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-True-idxmin] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-min] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap[True-4] 
dask/dataframe/tests/test_rolling.py::test_map_overlap[False-1] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-min] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-max] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-max] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-sum] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap[False-1] 
dask/dataframe/tests/test_rolling.py::test_map_overlap[False-4] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-sum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-prod] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-prod] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-first] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-first] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-last] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap[False-4] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-last] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-median] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-median] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-idxmax] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-idxmax] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-idxmin] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[disk-False-idxmin] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-min] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-min] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-max] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-max] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-sum] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-sum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-prod] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-prod] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-first] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-first] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-last] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-last] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-median] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-median] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-idxmax] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-idxmax] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-idxmin] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-None-idxmin] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-min] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-min] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-max] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-max] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-sum] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-sum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-prod] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-prod] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-first] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-first] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-last] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-last] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-median] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-median] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-idxmax] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-idxmax] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-idxmin] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-True-idxmin] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-min] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-min] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-max] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-max] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-sum] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-sum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-prod] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-prod] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-first] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-first] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-last] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-last] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-median] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-median] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-idxmax] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-idxmax] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-idxmin] 
[gw1] [ 82%] SKIPPED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_supported[tasks-False-idxmin] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-False-mean] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-False-mean] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-False-std] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-False-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-False-var] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-False-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-None-mean] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-None-mean] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-None-std] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-None-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-None-var] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[disk-None-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-False-mean] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-False-mean] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-False-std] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-False-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-False-var] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-False-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-None-mean] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-None-mean] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-None-std] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-None-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-None-var] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_not_implemented[tasks-None-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-min] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-min] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-max] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-max] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-sum] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-sum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-prod] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-prod] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-first] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-first] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-last] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-last] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-corr] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-False] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-corr] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-cov] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-cov] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-cumprod] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-False] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-False] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-False] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-False] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-False] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-False] 
[gw1] [ 82%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-cumprod] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-cumsum] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-False] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-False] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-False] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-False] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-True] 
[gw1] [ 82%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-cumsum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-mean] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-mean] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-median] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-median] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-std] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-std] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-var] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-var] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-min] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-min] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-max] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-max] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-sum] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-sum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-prod] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-prod] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-first] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-first] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-last] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-True] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-last] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-corr] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-corr] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-cov] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-True] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-False] 
[gw1] [ 82%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-cov] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-cumprod] 
[gw0] [ 82%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-True] 
[gw1] [ 83%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-cumprod] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-cumsum] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-True] 
[gw1] [ 83%] FAILED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-cumsum] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-mean] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-mean] 
dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-median] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-median] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-False-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-False-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-False-4-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-False-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-True-True-1] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-True-True-1] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-True-True-4] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-True-True-4] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-True-False-1] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-True-False-1] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-True-False-4] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-True-False-4] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-False-True-1] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-False-True-1] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-False-True-4] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-False-True-4] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-False-False-1] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-False-False-1] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-False-False-4] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[True-False-False-4] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-True-True-1] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-True-True-1] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-True-True-4] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-True-True-4] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-True-False-1] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-True-False-1] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-True-False-4] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-True-False-4] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-False-True-1] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-False-True-1] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-False-True-4] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-False-True-4] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-False-False-1] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-False-False-1] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-False-False-4] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_names[False-False-False-4] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_errors 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_errors 
dask/dataframe/tests/test_rolling.py::test_map_overlap_provide_meta 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_provide_meta 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-count-args0-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-count-args0-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-sum-args1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-mean-args2-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-median-args3-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-min-args4-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-max-args5-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-std-args6-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-var-args7-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-var-args7-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-skew-args8-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-kurt-args9-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-quantile-args10-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-apply-args11-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-1-apply-args11-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-count-args0-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-count-args0-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-sum-args1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-mean-args2-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-median-args3-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-min-args4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-max-args5-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-std-args6-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-var-args7-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-var-args7-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-skew-args8-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-kurt-args9-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-quantile-args10-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-apply-args11-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-2-apply-args11-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-count-args0-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-count-args0-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-sum-args1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-mean-args2-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-median-args3-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-min-args4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-max-args5-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-std-args6-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-True-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-var-args7-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-var-args7-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-skew-args8-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-True-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-kurt-args9-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-True-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-quantile-args10-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-apply-args11-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-True-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-False-1-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-False-1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-False-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-4-apply-args11-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-count-args0-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-count-args0-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-sum-args1-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-False-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-mean-args2-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-True-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-median-args3-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-min-args4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-True-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-max-args5-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-True-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-std-args6-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-var-args7-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-True-4-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-var-args7-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-skew-args8-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-False-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-kurt-args9-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-False-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-quantile-args10-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-False-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-apply-args11-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-False-4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-True-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-True-1-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-True-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[True-5-apply-args11-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-count-args0-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-True-4-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-count-args0-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-sum-args1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-mean-args2-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-median-args3-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-True-4-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-min-args4-False] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-False-1-True] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-max-args5-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-std-args6-True] 
[gw0] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-False-1-False] 
[gw1] [ 83%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-var-args7-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-False-4-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-var-args7-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-skew-args8-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-kurt-args9-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-quantile-args10-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-False-4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-apply-args11-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-True-1-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-True-1-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-True-4-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-1-apply-args11-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-count-args0-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-True-4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-count-args0-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-sum-args1-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-False-1-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-mean-args2-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-False-1-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-median-args3-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-False-4-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-min-args4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-max-args5-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-False-4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-std-args6-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup4-False-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-True-1-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-var-args7-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-True-1-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-var-args7-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-skew-args8-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-True-4-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-kurt-args9-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-quantile-args10-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-apply-args11-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-True-4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-2-apply-args11-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-count-args0-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-False-1-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-count-args0-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-sum-args1-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-False-1-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-mean-args2-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-False-4-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-median-args3-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-min-args4-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-False-4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-max-args5-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-True-1-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-std-args6-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-var-args7-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-True-1-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-var-args7-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-skew-args8-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-True-4-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-kurt-args9-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-quantile-args10-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-True-4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-apply-args11-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-False-1-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-False-1-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-False-4-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-4-apply-args11-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-count-args0-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-count-args0-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-sum-args1-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-False-4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-mean-args2-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-True-False-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-True-1-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-median-args3-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-True-1-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-min-args4-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-True-4-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-max-args5-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-std-args6-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-True-4-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-True-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-False-1-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-var-args7-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-False-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-False-1-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-var-args7-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-skew-args8-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-False-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-False-4-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-kurt-args9-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-quantile-args10-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-False-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-False-4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-apply-args11-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-True-False-4-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-True-1-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-True-1-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-True-1-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-True-1-False] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-True-4-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_methods[False-5-apply-args11-False] 
dask/dataframe/tests/test_rolling.py::test_rolling_cov[True-1] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-True-4-True] 
dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-True-4-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_map_overlap_multiple_dataframes[overlap_setup5-False-False-True-4-False] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-x-pandas] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_cov[True-1] 
dask/dataframe/tests/test_rolling.py::test_rolling_cov[True-2] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_cov[True-2] 
dask/dataframe/tests/test_rolling.py::test_rolling_cov[True-4] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_cov[True-4] 
dask/dataframe/tests/test_rolling.py::test_rolling_cov[True-5] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_cov[True-5] 
dask/dataframe/tests/test_rolling.py::test_rolling_cov[False-1] 
[gw0] [ 84%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-x-pandas] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-x-cudf] 
[gw0] [ 84%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-x-cudf] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-z-pandas] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_cov[False-1] 
dask/dataframe/tests/test_rolling.py::test_rolling_cov[False-2] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_cov[False-2] 
dask/dataframe/tests/test_rolling.py::test_rolling_cov[False-4] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_cov[False-4] 
dask/dataframe/tests/test_rolling.py::test_rolling_cov[False-5] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_cov[False-5] 
dask/dataframe/tests/test_rolling.py::test_rolling_names 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_names 
dask/dataframe/tests/test_rolling.py::test_rolling_partition_size 
[gw0] [ 84%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-z-pandas] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-z-cudf] 
[gw0] [ 84%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-z-cudf] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-by2-pandas] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_partition_size 
dask/dataframe/tests/test_rolling.py::test_time_rolling_constructor 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_constructor 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-count-args0-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-count-args0-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-sum-args1-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-mean-args2-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-median-args3-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-min-args4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-max-args5-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-std-args6-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-var-args7-True] 
[gw0] [ 84%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-by2-pandas] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-by2-cudf] 
[gw0] [ 84%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-by2-cudf] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-by3-pandas] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-var-args7-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-skew-args8-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-kurt-args9-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-quantile-args10-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-apply-args11-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[1s-apply-args11-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-count-args0-False] 
[gw0] [ 84%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-by3-pandas] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-by3-cudf] 
[gw0] [ 84%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-by3-cudf] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-True] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-count-args0-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-sum-args1-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-False] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-ascending2] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-ascending2] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-ascending3] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by0-ascending3] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-True] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-False] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-ascending2] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-mean-args2-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-ascending2] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-ascending3] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[10-by1-ascending3] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-True] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-True] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-False] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-ascending2] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-ascending2] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-ascending3] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by0-ascending3] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-median-args3-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-True] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-False] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-ascending2] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-ascending2] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-ascending3] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_single_partition[500-by1-ascending3] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-a-True-first] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-a-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-a-True-last] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-a-True-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-a-False-first] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-min-args4-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-a-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-a-False-last] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-a-False-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-b-True-first] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-b-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-b-True-last] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-b-True-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-b-False-first] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-b-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-b-False-last] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-b-False-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-by2-True-first] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-max-args5-False] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-by2-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-by2-True-last] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-by2-True-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-by2-False-first] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-by2-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-by2-False-last] 
[gw0] [ 84%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-1-by2-False-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-True-first] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-std-args6-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-var-args7-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-var-args7-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-skew-args8-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-kurt-args9-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-quantile-args10-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-apply-args11-False] 
[gw0] [ 84%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-True-last] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[2s-apply-args11-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-count-args0-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-count-args0-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-sum-args1-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-mean-args2-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-median-args3-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-min-args4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-max-args5-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-std-args6-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-var-args7-True] 
[gw0] [ 84%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-True-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-False-first] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-var-args7-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-skew-args8-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-kurt-args9-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-quantile-args10-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-apply-args11-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[3s-apply-args11-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-count-args0-False] 
[gw0] [ 84%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-False-last] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-count-args0-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-sum-args1-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-sum-args1-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-mean-args2-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-mean-args2-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-median-args3-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-median-args3-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-min-args4-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-min-args4-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-max-args5-False] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-max-args5-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-std-args6-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-std-args6-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-var-args7-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-var-args7-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-skew-args8-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-skew-args8-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-kurt-args9-True] 
[gw1] [ 84%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-kurt-args9-True] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-quantile-args10-False] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-quantile-args10-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-apply-args11-False] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-False-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-True-first] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_methods[window3-apply-args11-False] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_cov[1s] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_cov[1s] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_cov[2s] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_cov[2s] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_cov[3s] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-True-last] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_cov[3s] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_cov[window3] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_cov[window3] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[1s-10] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[1s-10] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[2s-10] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[2s-10] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[10s-10] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-True-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-False-first] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[10s-10] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[10h-10] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[10h-10] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[10s-100] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[10s-100] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[10h-100] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_fixed_chunks[10h-100] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[2s] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-False-last] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[2s] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[5s] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[5s] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[20s] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-False-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-True-first] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[20s] 
dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[10h] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-True-last] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling_large_window_variable_chunks[10h] 
dask/dataframe/tests/test_rolling.py::test_time_rolling[6s-6s] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling[6s-6s] 
dask/dataframe/tests/test_rolling.py::test_time_rolling[2s-2s] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling[2s-2s] 
dask/dataframe/tests/test_rolling.py::test_time_rolling[6s-2s] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_time_rolling[6s-2s] 
dask/dataframe/tests/test_rolling.py::test_rolling_agg_aggregate 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_rolling_agg_aggregate 
dask/dataframe/tests/test_rolling.py::test_rolling_numba_engine 
[gw1] [ 85%] SKIPPED dask/dataframe/tests/test_rolling.py::test_rolling_numba_engine 
dask/dataframe/tests/test_rolling.py::test_groupby_rolling 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-True-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-False-first] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_rolling.py::test_groupby_rolling 
dask/dataframe/tests/test_shuffle.py::test_shuffle[disk] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_shuffle[disk] 
dask/dataframe/tests/test_shuffle.py::test_shuffle[tasks] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_shuffle[tasks] 
dask/dataframe/tests/test_shuffle.py::test_default_partitions 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_default_partitions 
dask/dataframe/tests/test_shuffle.py::test_shuffle_npartitions[disk] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_shuffle_npartitions[disk] 
dask/dataframe/tests/test_shuffle.py::test_shuffle_npartitions[tasks] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_shuffle_npartitions[tasks] 
dask/dataframe/tests/test_shuffle.py::test_shuffle_npartitions_lt_input_partitions[disk] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-False-last] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_shuffle_npartitions_lt_input_partitions[disk] 
dask/dataframe/tests/test_shuffle.py::test_shuffle_npartitions_lt_input_partitions[tasks] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_shuffle_npartitions_lt_input_partitions[tasks] 
dask/dataframe/tests/test_shuffle.py::test_index_with_non_series[disk] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_index_with_non_series[disk] 
dask/dataframe/tests/test_shuffle.py::test_index_with_non_series[tasks] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_index_with_non_series[tasks] 
dask/dataframe/tests/test_shuffle.py::test_index_with_dataframe[disk] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_index_with_dataframe[disk] 
dask/dataframe/tests/test_shuffle.py::test_index_with_dataframe[tasks] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_index_with_dataframe[tasks] 
dask/dataframe/tests/test_shuffle.py::test_shuffle_from_one_partition_to_one_other[disk] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_shuffle_from_one_partition_to_one_other[disk] 
dask/dataframe/tests/test_shuffle.py::test_shuffle_from_one_partition_to_one_other[tasks] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_shuffle_from_one_partition_to_one_other[tasks] 
dask/dataframe/tests/test_shuffle.py::test_shuffle_empty_partitions[disk] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_shuffle_empty_partitions[disk] 
dask/dataframe/tests/test_shuffle.py::test_shuffle_empty_partitions[tasks] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_shuffle_empty_partitions[tasks] 
dask/dataframe/tests/test_shuffle.py::test_partitioning_index 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_partitioning_index 
dask/dataframe/tests/test_shuffle.py::test_partitioning_index_categorical_on_values 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_partitioning_index_categorical_on_values 
dask/dataframe/tests/test_shuffle.py::test_set_index_general[disk-1] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-False-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-a-True-first] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-a-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-a-True-last] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_general[disk-1] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-a-True-last] 
dask/dataframe/tests/test_shuffle.py::test_set_index_general[disk-4] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-a-False-first] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-a-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-a-False-last] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-a-False-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-b-True-first] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-b-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-b-True-last] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-b-True-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-b-False-first] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-b-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-b-False-last] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-b-False-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-by2-True-first] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-by2-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-by2-True-last] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-by2-True-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-by2-False-first] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-by2-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-by2-False-last] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-1-by2-False-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-True-first] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_general[disk-4] 
dask/dataframe/tests/test_shuffle.py::test_set_index_general[disk-7] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-True-last] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_general[disk-7] 
dask/dataframe/tests/test_shuffle.py::test_set_index_general[disk-23] 
[gw1] [ 85%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_set_index_general[disk-23] 
dask/dataframe/tests/test_shuffle.py::test_set_index_general[tasks-1] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_general[tasks-1] 
dask/dataframe/tests/test_shuffle.py::test_set_index_general[tasks-4] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-True-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-False-first] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_general[tasks-4] 
dask/dataframe/tests/test_shuffle.py::test_set_index_general[tasks-7] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-False-last] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_general[tasks-7] 
dask/dataframe/tests/test_shuffle.py::test_set_index_general[tasks-23] 
[gw1] [ 85%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_set_index_general[tasks-23] 
dask/dataframe/tests/test_shuffle.py::test_set_index_string[disk-string[python]] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-False-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-b-True-first] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[disk-string[python]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_string[disk-string[pyarrow]] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-b-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-b-True-last] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[disk-string[pyarrow]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_string[disk-object] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[disk-object] 
dask/dataframe/tests/test_shuffle.py::test_set_index_string[tasks-string[python]] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-b-True-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-b-False-first] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-b-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-b-False-last] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-b-False-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-True-first] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[tasks-string[python]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_string[tasks-string[pyarrow]] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-True-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-True-last] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[tasks-string[pyarrow]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_string[tasks-object] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-True-last] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-False-first] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[tasks-object] 
dask/dataframe/tests/test_shuffle.py::test_set_index_self_index[disk] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-False-first] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-False-last] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_self_index[disk] 
dask/dataframe/tests/test_shuffle.py::test_set_index_self_index[tasks] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_self_index[tasks] 
dask/dataframe/tests/test_shuffle.py::test_set_index_names[disk] 
[gw1] [ 85%] XFAIL dask/dataframe/tests/test_shuffle.py::test_set_index_names[disk] 
dask/dataframe/tests/test_shuffle.py::test_set_index_names[tasks] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_names[tasks] 
dask/dataframe/tests/test_shuffle.py::test_set_index_2[disk] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-False-last] 
dask/dataframe/tests/test_shuffle.py::test_shuffle_values_raises 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_shuffle_values_raises 
dask/dataframe/tests/test_shuffle.py::test_shuffle_by_as_list 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_2[disk] 
dask/dataframe/tests/test_shuffle.py::test_set_index_2[tasks] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_shuffle_by_as_list 
dask/dataframe/tests/test_shuffle.py::test_sort_values_custom_function[1-by0] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_custom_function[1-by0] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_custom_function[1-by1] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_custom_function[1-by1] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_custom_function[10-by0] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_2[tasks] 
dask/dataframe/tests/test_shuffle.py::test_set_index_3[disk] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_custom_function[10-by0] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_custom_function[10-by1] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_3[disk] 
dask/dataframe/tests/test_shuffle.py::test_set_index_3[tasks] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_custom_function[10-by1] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_bool_ascending 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_3[tasks] 
dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[True-True] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[True-True] 
dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[True-False] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_bool_ascending 
dask/dataframe/tests/test_shuffle.py::test_sort_values_timestamp[1] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_timestamp[1] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_timestamp[3] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[True-False] 
dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[False-True] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_timestamp[3] 
dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data0-string[pyarrow]] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data0-string[pyarrow]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data1-binary[pyarrow]] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data1-binary[pyarrow]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data2-int64[pyarrow]] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data2-int64[pyarrow]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data3-float64[pyarrow]] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data3-float64[pyarrow]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data4-uint64[pyarrow]] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[False-True] 
dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[False-False] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data4-uint64[pyarrow]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data5-date32[pyarrow]] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data5-date32[pyarrow]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data6-timestamp[ns][pyarrow]] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data6-timestamp[ns][pyarrow]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data7-decimal128] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data7-decimal128] 
dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data8-duration[ns][pyarrow]] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data8-duration[ns][pyarrow]] 
dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data9-time64[ns][pyarrow]] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[False-False] 
dask/dataframe/tests/test_shuffle.py::test_shuffle_sort[disk] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data9-time64[ns][pyarrow]] 
dask/dataframe/tests/test_shuffle.py::test_shuffle_nulls_introduced 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_shuffle_sort[disk] 
dask/dataframe/tests/test_shuffle.py::test_shuffle_sort[tasks] 
[gw0] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_shuffle_nulls_introduced 
dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input0] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input1] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input2] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input3] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input4] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_shuffle_sort[tasks] 
dask/dataframe/tests/test_shuffle.py::test_maybe_buffered_partd 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_maybe_buffered_partd 
dask/dataframe/tests/test_shuffle.py::test_set_index_with_explicit_divisions 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input5] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[conj-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input0] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input1] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input2] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input3] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_with_explicit_divisions 
dask/dataframe/tests/test_shuffle.py::test_set_index_with_empty_divisions 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_with_empty_divisions 
dask/dataframe/tests/test_shuffle.py::test_set_index_divisions_2 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input4] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input5] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input0] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input1] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input2] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input3] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input4] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input5] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_divisions_2 
dask/dataframe/tests/test_shuffle.py::test_set_index_consistent_divisions 
[gw1] [ 85%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_set_index_consistent_divisions 
dask/dataframe/tests/test_shuffle.py::test_set_index_detects_sorted_data[disk] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_detects_sorted_data[disk] 
dask/dataframe/tests/test_shuffle.py::test_set_index_detects_sorted_data[tasks] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_detects_sorted_data[tasks] 
dask/dataframe/tests/test_shuffle.py::test_set_index_sorts 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input0] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_sorts 
dask/dataframe/tests/test_shuffle.py::test_set_index[pandas] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input1] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input2] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input3] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input4] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input5] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log2-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input0] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input1] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input2] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input3] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index[pandas] 
dask/dataframe/tests/test_shuffle.py::test_set_index[cudf] 
[gw1] [ 85%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_set_index[cudf] 
dask/dataframe/tests/test_shuffle.py::test_set_index_interpolate[pandas] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input4] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_interpolate[pandas] 
dask/dataframe/tests/test_shuffle.py::test_set_index_interpolate[cudf] 
[gw1] [ 85%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_set_index_interpolate[cudf] 
dask/dataframe/tests/test_shuffle.py::test_set_index_interpolate_int[pandas] 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_interpolate_int[pandas] 
dask/dataframe/tests/test_shuffle.py::test_set_index_interpolate_int[cudf] 
[gw1] [ 85%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_set_index_interpolate_int[cudf] 
dask/dataframe/tests/test_shuffle.py::test_set_index_npartitions 
[gw1] [ 85%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_npartitions 
dask/dataframe/tests/test_shuffle.py::test_set_index_datetime_precision[ns] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input5] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log10-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input0] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input1] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input2] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input3] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input4] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input5] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[log1p-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input0] 
[gw1] [ 85%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_datetime_precision[ns] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input0] 
dask/dataframe/tests/test_shuffle.py::test_set_index_datetime_precision[us] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input1] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input2] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input3] 
[gw0] [ 85%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[expm1-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input2] 
[gw1] [ 86%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_datetime_precision[us] 
dask/dataframe/tests/test_shuffle.py::test_set_index_drop[True] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sqrt-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input5] 
[gw1] [ 86%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_drop[True] 
dask/dataframe/tests/test_shuffle.py::test_set_index_drop[False] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[square-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sin-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cos-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input1] 
[gw1] [ 86%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_drop[False] 
dask/dataframe/tests/test_shuffle.py::test_set_index_raises_error_on_bad_input 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_raises_error_on_bad_input 
dask/dataframe/tests/test_shuffle.py::test_set_index_sorted_true 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input4] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_sorted_true 
dask/dataframe/tests/test_shuffle.py::test_set_index_sorted_single_partition 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_sorted_single_partition 
dask/dataframe/tests/test_shuffle.py::test_set_index_sorted_min_max_same 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input4] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_sorted_min_max_same 
dask/dataframe/tests/test_shuffle.py::test_set_index_empty_partition 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tan-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsin-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input2] 
[gw1] [ 86%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_empty_partition 
dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[int] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccos-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input5] 
[gw1] [ 86%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[int] 
dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[float] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctan-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sinh-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input2] 
[gw1] [ 86%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[float] 
dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[str] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cosh-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input5] 
[gw1] [ 86%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[str] 
dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[<lambda>] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[tanh-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arcsinh-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input1] 
[gw1] [ 86%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[<lambda>] 
dask/dataframe/tests/test_shuffle.py::test_set_index_categorical 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input2] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_categorical 
dask/dataframe/tests/test_shuffle.py::test_set_index_with_empty_and_overlap 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_with_empty_and_overlap 
dask/dataframe/tests/test_shuffle.py::test_compute_divisions 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input3] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_compute_divisions 
dask/dataframe/tests/test_shuffle.py::test_empty_partitions 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arccosh-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input0] 
[gw1] [ 86%] FAILED dask/dataframe/tests/test_shuffle.py::test_empty_partitions 
dask/dataframe/tests/test_shuffle.py::test_gh_2730 
[gw1] [ 86%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_gh_2730 
dask/dataframe/tests/test_shuffle.py::test_set_index_timestamp 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[arctanh-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input2] 
[gw1] [ 86%] FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_timestamp 
dask/dataframe/tests/test_shuffle.py::test_set_index_ea_dtype 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input3] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_ea_dtype 
dask/dataframe/tests/test_shuffle.py::test_disk_shuffle_with_compression_option[None] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_disk_shuffle_with_compression_option[None] 
dask/dataframe/tests/test_shuffle.py::test_disk_shuffle_with_compression_option[ZLib] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_disk_shuffle_with_compression_option[ZLib] 
dask/dataframe/tests/test_shuffle.py::test_disk_shuffle_with_unknown_compression 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input4] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_disk_shuffle_with_unknown_compression 
dask/dataframe/tests/test_shuffle.py::test_disk_shuffle_check_actual_compression 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[deg2rad-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input1] 
[gw1] [ 86%] FAILED dask/dataframe/tests/test_shuffle.py::test_disk_shuffle_check_actual_compression 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-id-None] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rad2deg-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input5] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-id-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-id-True] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isfinite-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isinf-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input3] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-id-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-id-False] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[isnan-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[signbit-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input1] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-id-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-name-None] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[degrees-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input4] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-name-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-name-True] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[radians-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[rint-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input0] 
[gw1] [ 86%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-name-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-name-False] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input2] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input3] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input4] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input5] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[fabs-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input0] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input1] 
[gw0] [ 86%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input2] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input3] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input4] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input5] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[sign-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input0] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input1] 
[gw1] [ 87%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-name-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on2-None] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input2] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input3] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input4] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input5] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[absolute-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input0] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input1] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input2] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input3] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input4] 
[gw1] [ 87%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on2-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on2-True] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input5] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[floor-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input0] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input1] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input2] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input3] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input4] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input5] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[ceil-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input0] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input1] 
[gw1] [ 87%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on2-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on2-False] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input2] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input3] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input4] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input5] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[trunc-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input0] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input1] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input2] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input3] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input4] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-equal] 
[gw1] [ 87%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on2-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on3-None] 
[gw0] [ 87%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-equal] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_or1] 
[gw0] [ 87%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_or1] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_and1] 
[gw1] [ 87%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on3-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on3-True] 
[gw0] [ 87%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_and1] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_xor1] 
[gw0] [ 87%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_xor1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-conj-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-conj-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-conj-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-conj-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-conj-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-conj-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-conj-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-conj-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-conj-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-conj-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log2-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log2-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log2-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log2-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log2-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log2-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log2-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log2-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log2-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log2-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log10-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log10-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log10-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log10-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log10-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log10-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log10-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log10-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log10-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log10-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log1p-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log1p-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log1p-prod] 
[gw1] [ 87%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on3-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on3-False] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log1p-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log1p-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log1p-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log1p-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log1p-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log1p-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-log1p-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-expm1-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-expm1-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-expm1-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-expm1-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-expm1-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-expm1-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-expm1-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-expm1-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-expm1-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-expm1-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sqrt-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sqrt-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sqrt-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sqrt-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sqrt-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sqrt-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sqrt-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sqrt-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sqrt-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sqrt-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-square-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-square-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-square-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-square-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-square-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-square-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-square-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-square-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-square-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-square-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sin-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sin-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sin-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sin-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sin-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sin-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sin-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sin-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sin-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sin-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cos-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cos-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cos-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cos-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cos-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cos-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cos-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cos-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cos-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cos-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tan-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tan-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tan-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tan-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tan-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tan-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tan-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tan-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tan-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tan-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsin-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsin-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsin-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsin-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsin-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsin-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsin-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsin-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsin-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsin-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccos-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccos-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccos-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccos-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccos-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccos-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccos-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccos-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccos-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccos-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctan-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctan-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctan-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctan-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctan-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctan-min] 
[gw1] [ 87%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-None-on3-False] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctan-max] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-id-None] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctan-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctan-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctan-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sinh-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sinh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sinh-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sinh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sinh-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sinh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sinh-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sinh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sinh-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sinh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cosh-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cosh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cosh-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cosh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cosh-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cosh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cosh-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cosh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cosh-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cosh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tanh-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tanh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tanh-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tanh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tanh-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tanh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tanh-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tanh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tanh-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-tanh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsinh-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsinh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsinh-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsinh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsinh-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsinh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsinh-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsinh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsinh-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arcsinh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccosh-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccosh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccosh-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccosh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccosh-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccosh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccosh-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccosh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccosh-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arccosh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctanh-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctanh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctanh-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctanh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctanh-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctanh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctanh-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctanh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctanh-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-arctanh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-deg2rad-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-deg2rad-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-deg2rad-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-deg2rad-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-deg2rad-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-deg2rad-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-deg2rad-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-deg2rad-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-deg2rad-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-deg2rad-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rad2deg-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rad2deg-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rad2deg-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rad2deg-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rad2deg-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rad2deg-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rad2deg-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rad2deg-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rad2deg-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rad2deg-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isfinite-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isfinite-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isfinite-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isfinite-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isfinite-min] 
[gw1] [ 87%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-id-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-id-True] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isfinite-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isfinite-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isfinite-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isfinite-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isfinite-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isinf-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isinf-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isinf-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isinf-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isinf-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isinf-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isinf-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isinf-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isinf-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isinf-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isnan-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isnan-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isnan-prod] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isnan-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isnan-min] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isnan-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isnan-max] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isnan-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isnan-mean] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-isnan-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-signbit-sum] 
[gw0] [ 87%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-signbit-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-signbit-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-signbit-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-signbit-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-signbit-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-signbit-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-signbit-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-signbit-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-signbit-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-degrees-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-degrees-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-degrees-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-degrees-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-degrees-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-degrees-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-degrees-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-degrees-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-degrees-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-degrees-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-radians-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-radians-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-radians-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-radians-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-radians-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-radians-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-radians-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-radians-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-radians-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-radians-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rint-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rint-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rint-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rint-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rint-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rint-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rint-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rint-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rint-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-rint-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-fabs-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-fabs-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-fabs-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-fabs-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-fabs-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-fabs-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-fabs-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-fabs-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-fabs-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-fabs-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sign-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sign-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sign-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sign-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sign-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sign-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sign-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sign-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sign-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-sign-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-absolute-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-absolute-sum] 
[gw1] [ 88%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-id-True] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-absolute-prod] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-id-False] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-absolute-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-absolute-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-absolute-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-absolute-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-absolute-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-absolute-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-absolute-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-floor-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-floor-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-floor-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-floor-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-floor-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-floor-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-floor-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-floor-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-floor-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-floor-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-ceil-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-ceil-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-ceil-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-ceil-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-ceil-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-ceil-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-ceil-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-ceil-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-ceil-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-ceil-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-trunc-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-trunc-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-trunc-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-trunc-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-trunc-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-trunc-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-trunc-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-trunc-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-trunc-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-trunc-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-logical_not-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-logical_not-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-logical_not-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-logical_not-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-logical_not-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-logical_not-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-logical_not-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-logical_not-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-logical_not-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-logical_not-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cbrt-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cbrt-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cbrt-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cbrt-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cbrt-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cbrt-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cbrt-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cbrt-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cbrt-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-cbrt-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp2-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp2-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp2-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp2-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp2-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp2-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp2-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp2-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp2-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-exp2-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-negative-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-negative-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-negative-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-negative-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-negative-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-negative-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-negative-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-negative-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-negative-mean] 
[gw1] [ 88%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-id-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-name-None] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-negative-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-reciprocal-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-reciprocal-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-reciprocal-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-reciprocal-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-reciprocal-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-reciprocal-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-reciprocal-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-reciprocal-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-reciprocal-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-reciprocal-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-spacing-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-spacing-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-spacing-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-spacing-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-spacing-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-spacing-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-spacing-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-spacing-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-spacing-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas0-spacing-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-conj-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-conj-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-conj-prod] 
[gw0] [ 88%] XFAIL dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-conj-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-conj-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-conj-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-conj-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-conj-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-conj-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-conj-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log-sum] 
[gw1] [ 88%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-name-None] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log-sum] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-name-True] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log2-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log2-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log2-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log2-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log2-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log2-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log2-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log2-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log2-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log2-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log10-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log10-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log10-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log10-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log10-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log10-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log10-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log10-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log10-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log10-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log1p-sum] 
[gw1] [ 88%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-name-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-name-False] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log1p-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log1p-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log1p-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log1p-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log1p-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log1p-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log1p-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log1p-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-log1p-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-expm1-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-expm1-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-expm1-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-expm1-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-expm1-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-expm1-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-expm1-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-expm1-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-expm1-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-expm1-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sqrt-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sqrt-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sqrt-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sqrt-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sqrt-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sqrt-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sqrt-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sqrt-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sqrt-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sqrt-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-square-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-square-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-square-prod] 
[gw1] [ 88%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-name-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on2-None] 
[gw0] [ 88%] XFAIL dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-square-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-square-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-square-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-square-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-square-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-square-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-square-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sin-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sin-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sin-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sin-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sin-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sin-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sin-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sin-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sin-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sin-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cos-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cos-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cos-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cos-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cos-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cos-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cos-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cos-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cos-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cos-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tan-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tan-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tan-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tan-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tan-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tan-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tan-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tan-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tan-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tan-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsin-sum] 
[gw1] [ 88%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on2-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on2-True] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsin-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsin-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsin-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsin-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsin-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsin-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsin-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsin-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsin-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccos-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccos-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccos-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccos-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccos-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccos-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccos-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccos-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccos-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccos-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctan-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctan-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctan-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctan-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctan-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctan-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctan-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctan-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctan-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctan-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sinh-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sinh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sinh-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sinh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sinh-min] 
[gw1] [ 88%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on2-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on2-False] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sinh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sinh-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sinh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sinh-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sinh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cosh-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cosh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cosh-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cosh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cosh-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cosh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cosh-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cosh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cosh-mean] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cosh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tanh-sum] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tanh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tanh-prod] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tanh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tanh-min] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tanh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tanh-max] 
[gw0] [ 88%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tanh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tanh-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-tanh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsinh-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsinh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsinh-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsinh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsinh-min] 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on2-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on3-None] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsinh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsinh-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsinh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsinh-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arcsinh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccosh-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccosh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccosh-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccosh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccosh-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccosh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccosh-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccosh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccosh-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arccosh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctanh-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctanh-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctanh-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctanh-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctanh-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctanh-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctanh-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctanh-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctanh-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-arctanh-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-deg2rad-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-deg2rad-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-deg2rad-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-deg2rad-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-deg2rad-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-deg2rad-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-deg2rad-max] 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on3-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on3-True] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-deg2rad-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-deg2rad-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-deg2rad-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rad2deg-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rad2deg-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rad2deg-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rad2deg-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rad2deg-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rad2deg-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rad2deg-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rad2deg-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rad2deg-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rad2deg-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isfinite-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isfinite-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isfinite-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isfinite-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isfinite-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isfinite-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isfinite-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isfinite-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isfinite-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isfinite-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isinf-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isinf-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isinf-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isinf-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isinf-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isinf-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isinf-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isinf-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isinf-mean] 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on3-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on3-False] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isinf-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isnan-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isnan-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isnan-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isnan-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isnan-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isnan-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isnan-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isnan-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isnan-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-isnan-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-signbit-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-signbit-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-signbit-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-signbit-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-signbit-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-signbit-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-signbit-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-signbit-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-signbit-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-signbit-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-degrees-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-degrees-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-degrees-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-degrees-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-degrees-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-degrees-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-degrees-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-degrees-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-degrees-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-degrees-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-radians-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-radians-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-radians-prod] 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[disk-4-on3-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-id-None] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-radians-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-radians-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-radians-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-radians-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-radians-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-radians-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-radians-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rint-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rint-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rint-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rint-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rint-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rint-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rint-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rint-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rint-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-rint-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-fabs-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-fabs-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-fabs-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-fabs-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-fabs-min] 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-id-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-id-True] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-fabs-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-fabs-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-fabs-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-fabs-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-fabs-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sign-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sign-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sign-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sign-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sign-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sign-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sign-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sign-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sign-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-sign-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-absolute-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-absolute-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-absolute-prod] 
[gw0] [ 89%] XFAIL dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-absolute-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-absolute-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-absolute-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-absolute-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-absolute-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-absolute-mean] 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-id-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-id-False] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-absolute-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-floor-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-floor-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-floor-prod] 
[gw0] [ 89%] SKIPPED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-floor-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-floor-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-floor-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-floor-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-floor-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-floor-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-floor-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-ceil-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-ceil-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-ceil-prod] 
[gw0] [ 89%] SKIPPED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-ceil-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-ceil-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-ceil-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-ceil-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-ceil-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-ceil-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-ceil-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-trunc-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-trunc-sum] 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-id-False] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-trunc-prod] 
[gw0] [ 89%] SKIPPED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-trunc-prod] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-name-None] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-trunc-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-trunc-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-trunc-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-trunc-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-trunc-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-trunc-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-logical_not-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-logical_not-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-logical_not-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-logical_not-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-logical_not-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-logical_not-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-logical_not-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-logical_not-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-logical_not-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-logical_not-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cbrt-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cbrt-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cbrt-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cbrt-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cbrt-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cbrt-min] 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-name-None] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cbrt-max] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-name-True] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cbrt-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cbrt-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-cbrt-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp2-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp2-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp2-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp2-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp2-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp2-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp2-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp2-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp2-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-exp2-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-negative-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-negative-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-negative-prod] 
[gw0] [ 89%] XFAIL dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-negative-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-negative-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-negative-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-negative-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-negative-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-negative-mean] 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-name-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-name-False] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-negative-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-reciprocal-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-reciprocal-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-reciprocal-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-reciprocal-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-reciprocal-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-reciprocal-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-reciprocal-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-reciprocal-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-reciprocal-mean] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-reciprocal-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-spacing-sum] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-spacing-sum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-spacing-prod] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-spacing-prod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-spacing-min] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-spacing-min] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-spacing-max] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-spacing-max] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-spacing-mean] 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-name-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on2-None] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_reduction[pandas1-spacing-mean] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[15-pandas0] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[15-pandas0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[15-pandas1] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[15-pandas1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[16.4_0-pandas0] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[16.4_0-pandas0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[16.4_0-pandas1] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[16.4_0-pandas1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[scalar2-pandas0] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[scalar2-pandas0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[scalar2-pandas1] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[scalar2-pandas1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[16.4_1-pandas0] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[16.4_1-pandas0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[16.4_1-pandas1] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_numpy_scalar_comparison[16.4_1-pandas1] 
dask/dataframe/tests/test_utils_dataframe.py::test_make_meta 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_make_meta 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_nonempty 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_nonempty 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_duplicated 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_duplicated 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_nonempty_empty_categories 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_nonempty_empty_categories 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_nonempty_index 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_nonempty_index 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_nonempty_uint64index 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_nonempty_uint64index 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_nonempty_scalar 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_nonempty_scalar 
dask/dataframe/tests/test_utils_dataframe.py::test_raise_on_meta_error 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_raise_on_meta_error 
dask/dataframe/tests/test_utils_dataframe.py::test_check_meta 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_check_meta 
dask/dataframe/tests/test_utils_dataframe.py::test_check_matching_columns_raises_appropriate_errors 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_check_matching_columns_raises_appropriate_errors 
dask/dataframe/tests/test_utils_dataframe.py::test_check_meta_typename 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_check_meta_typename 
dask/dataframe/tests/test_utils_dataframe.py::test_is_dataframe_like[True] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_is_dataframe_like[True] 
dask/dataframe/tests/test_utils_dataframe.py::test_is_dataframe_like[False] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_is_dataframe_like[False] 
dask/dataframe/tests/test_utils_dataframe.py::test_apply_and_enforce_message 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_apply_and_enforce_message 
dask/dataframe/tests/test_utils_dataframe.py::test_nonempty_series_sparse 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_nonempty_series_sparse 
dask/dataframe/tests/test_utils_dataframe.py::test_nonempty_series_nullable_float 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_nonempty_series_nullable_float 
dask/dataframe/tests/test_utils_dataframe.py::test_assert_eq_sorts 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on2-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on2-True] 
[gw1] [ 89%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on2-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on2-False] 
[gw0] [ 89%] FAILED dask/dataframe/tests/test_utils_dataframe.py::test_assert_eq_sorts 
dask/dataframe/tests/test_utils_dataframe.py::test_assert_eq_scheduler 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_assert_eq_scheduler 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data0] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data0] 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data1] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data1] 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data2] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data2] 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data3] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data3] 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data4] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data4] 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data5] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities[data5] 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities_raise[data0] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities_raise[data0] 
dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities_raise[data1] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_meta_constructor_utilities_raise[data1] 
dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions0-True] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions0-True] 
dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions1-False] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions1-False] 
dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions2-False] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions2-False] 
dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions3-True] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions3-True] 
dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions4-True] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions4-True] 
dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[123-False] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[123-False] 
dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions6-False] 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_valid_divisions[divisions6-False] 
dask/dataframe/tests/test_utils_dataframe.py::test_pyarrow_strings_enabled 
[gw0] [ 89%] PASSED dask/dataframe/tests/test_utils_dataframe.py::test_pyarrow_strings_enabled 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-30min-right-right] 
[gw0] [ 89%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-30min-right-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on2-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on3-None] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-30min-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-30min-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-h-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-h-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-h-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-h-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-D-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-D-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-D-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-D-left-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on3-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on3-True] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-W-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-W-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-W-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-W-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-ME-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-ME-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-ME-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-ME-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-2-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-30min-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-30min-right-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on3-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on3-False] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-30min-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-30min-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-h-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-h-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-h-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-h-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-D-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-D-right-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-None-on3-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-id-None] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-D-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-D-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-D-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-W-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-W-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-W-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-W-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-ME-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-ME-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-ME-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-ME-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-count-5-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-30min-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-30min-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-30min-left-right] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-id-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-id-True] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-30min-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-h-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-h-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-h-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-h-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-D-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-D-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-D-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-D-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-W-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-W-right-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-id-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-id-False] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-W-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-W-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-ME-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-ME-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-ME-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-ME-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-2-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-30min-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-30min-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-30min-left-right] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-id-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-name-None] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-30min-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-h-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-h-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-h-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-h-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-D-right-right] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-name-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-name-True] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-D-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-D-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-D-left-right] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-name-True] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-D-left-left] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-name-False] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-W-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-W-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-W-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-W-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-ME-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-ME-right-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-name-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on2-None] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-ME-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-ME-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-mean-5-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-30min-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-30min-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-30min-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-30min-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-h-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-h-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-h-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-h-left-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on2-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on2-True] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-D-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-D-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-D-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-D-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-W-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-W-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-W-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-W-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-ME-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-ME-right-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on2-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on2-False] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-ME-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-ME-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-2-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-30min-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-30min-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-30min-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-30min-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-h-right-right] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on2-False] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on3-None] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-h-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-h-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-h-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-D-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-D-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-D-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-D-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-D-left-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on3-None] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on3-True] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-W-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-W-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-W-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-W-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-ME-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-ME-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-ME-left-right] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on3-True] 
dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on3-False] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-ME-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[series-ohlc-5-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-30min-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-30min-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-30min-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-30min-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-h-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-h-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-h-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-h-left-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_dataframe_shuffle_on_arg[tasks-4-on3-False] 
dask/dataframe/tests/test_shuffle.py::test_set_index_overlap 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-D-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-D-right-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_overlap 
dask/dataframe/tests/test_shuffle.py::test_set_index_overlap_2 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-D-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-D-left-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_overlap_2 
dask/dataframe/tests/test_shuffle.py::test_set_index_overlap_does_not_drop_rows_when_divisions_overlap 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-W-right-right] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_overlap_does_not_drop_rows_when_divisions_overlap 
dask/dataframe/tests/test_shuffle.py::test_compute_current_divisions_nan_partition 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-W-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-W-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-W-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-ME-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-ME-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-ME-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-ME-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-2-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-30min-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-30min-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-30min-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-30min-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-h-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-h-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-h-left-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-h-left-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-D-right-right] 
[gw1] [ 90%] FAILED dask/dataframe/tests/test_shuffle.py::test_compute_current_divisions_nan_partition 
dask/dataframe/tests/test_shuffle.py::test_compute_current_divisions_overlap 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-D-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-D-left-right] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_compute_current_divisions_overlap 
dask/dataframe/tests/test_shuffle.py::test_compute_current_divisions_overlap_2 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_compute_current_divisions_overlap_2 
dask/dataframe/tests/test_shuffle.py::test_set_index_nan_partition 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-D-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-D-left-left] 
[gw1] [ 90%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_nan_partition 
dask/dataframe/tests/test_shuffle.py::test_set_index_with_dask_dt_index 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-W-right-right] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-W-right-left] 
[gw0] [ 90%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-W-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-W-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-ME-right-right] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_with_dask_dt_index 
dask/dataframe/tests/test_shuffle.py::test_set_index_with_series_uses_fastpath 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-ME-right-left] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_shuffle.py::test_set_index_with_series_uses_fastpath 
dask/dataframe/tests/test_shuffle.py::test_sort_values[10-a-True] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-ME-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-ME-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-count-5-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-30min-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-30min-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-30min-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-30min-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-h-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-h-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-h-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-h-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-D-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-D-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-D-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-D-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-W-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-W-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-W-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-W-left-left] 
[gw1] [ 91%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[10-a-True] 
dask/dataframe/tests/test_shuffle.py::test_sort_values[10-a-False] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-ME-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-ME-right-left] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values[10-a-False] 
dask/dataframe/tests/test_shuffle.py::test_sort_values[10-b-True] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-ME-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-ME-left-left] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values[10-b-True] 
dask/dataframe/tests/test_shuffle.py::test_sort_values[10-b-False] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-2-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-30min-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-30min-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-30min-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-30min-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-h-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-h-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-h-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-h-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-D-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-D-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-D-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-D-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-D-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-W-right-right] 
[gw1] [ 91%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[10-b-False] 
dask/dataframe/tests/test_shuffle.py::test_sort_values[10-by2-True] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-W-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-W-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-W-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-ME-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-ME-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-ME-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-ME-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-mean-5-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-30min-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-30min-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-30min-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-30min-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-h-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-h-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-h-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-h-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-D-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-D-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-D-left-right] 
[gw1] [ 91%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[10-by2-True] 
dask/dataframe/tests/test_shuffle.py::test_sort_values[10-by2-False] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-D-left-left] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values[10-by2-False] 
dask/dataframe/tests/test_shuffle.py::test_sort_values[500-a-True] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-W-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-W-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-W-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-W-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-ME-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-ME-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-ME-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-ME-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-2-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30min-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30min-right-left] 
[gw1] [ 91%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[500-a-True] 
dask/dataframe/tests/test_shuffle.py::test_sort_values[500-a-False] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values[500-a-False] 
dask/dataframe/tests/test_shuffle.py::test_sort_values[500-b-True] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30min-left-right] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values[500-b-True] 
dask/dataframe/tests/test_shuffle.py::test_sort_values[500-b-False] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30min-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-D-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-D-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-D-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-D-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-D-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-D-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-D-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-W-right-right] 
[gw1] [ 91%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[500-b-False] 
dask/dataframe/tests/test_shuffle.py::test_sort_values[500-by2-True] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-W-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-W-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-W-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-ME-right-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-ME-right-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-ME-right-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-ME-right-left] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-ME-left-right] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-ME-left-right] 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-ME-left-left] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample[frame-ohlc-5-ME-left-left] 
dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[count] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[count] 
dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[nunique] 
[gw1] [ 91%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[500-by2-True] 
dask/dataframe/tests/test_shuffle.py::test_sort_values[500-by2-False] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[nunique] 
dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[size] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values[500-by2-False] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-x-pandas] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[size] 
dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[sum] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_resample_has_correct_fill_value[sum] 
dask/dataframe/tseries/tests/test_resample.py::test_resample_agg 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_resample_agg 
dask/dataframe/tseries/tests/test_resample.py::test_resample_agg_passes_kwargs 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-x-pandas] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-x-cudf] 
[gw1] [ 91%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-x-cudf] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-z-pandas] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_resample_agg_passes_kwargs 
dask/dataframe/tseries/tests/test_resample.py::test_resample_throws_error_when_parition_index_does_not_match_index 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_resample_throws_error_when_parition_index_does_not_match_index 
dask/dataframe/tseries/tests/test_resample.py::test_resample_pads_last_division_to_avoid_off_by_one 
[gw0] [ 91%] FAILED dask/dataframe/tseries/tests/test_resample.py::test_resample_pads_last_division_to_avoid_off_by_one 
dask/dataframe/tseries/tests/test_resample.py::test_resample_does_not_evenly_divide_day 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_resample_does_not_evenly_divide_day 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample_does_not_evenly_divide_day 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample_does_not_evenly_divide_day 
dask/dataframe/tseries/tests/test_resample.py::test_unknown_divisions_error 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_unknown_divisions_error 
dask/dataframe/tseries/tests/test_resample.py::test_resample_index_name 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_resample_index_name 
dask/dataframe/tseries/tests/test_resample.py::test_series_resample_non_existent_datetime 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_series_resample_non_existent_datetime 
dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[nunique] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[nunique] 
dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[mean] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[mean] 
dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[count] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[count] 
dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[size] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[size] 
dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[quantile] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_common_aggs[quantile] 
dask/dataframe/tseries/tests/test_resample.py::test_rule_deprecated 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample.py::test_rule_deprecated 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[count-kwargs0] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[count-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[count-kwargs1] 
[gw1] [ 91%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-z-pandas] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-z-cudf] 
[gw1] [ 91%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-z-cudf] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-by2-pandas] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-by2-pandas] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-by2-cudf] 
[gw1] [ 91%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-by2-cudf] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-by3-pandas] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[count-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[prod-kwargs0] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[prod-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[prod-kwargs1] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[prod-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[mean-kwargs0] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[mean-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[mean-kwargs1] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[mean-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[sum-kwargs0] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[sum-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[sum-kwargs1] 
[gw1] [ 91%] FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-by3-pandas] 
dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-by3-cudf] 
[gw1] [ 91%] SKIPPED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-by3-cudf] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input5] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[sum-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[min-kwargs0] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[logical_not-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input0] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input1] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[min-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[min-kwargs1] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input2] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[min-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[max-kwargs0] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input3] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input4] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input5] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[max-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[max-kwargs1] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[cbrt-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input0] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[max-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[first-kwargs0] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input1] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input2] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[first-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[first-kwargs1] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input3] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input4] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input5] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[first-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[last-kwargs0] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[exp2-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input0] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input1] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[last-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[last-kwargs1] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input2] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input3] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[last-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[var-kwargs0] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input4] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input5] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[var-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[var-kwargs1] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[negative-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input0] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input1] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[var-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[std-kwargs0] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input2] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input3] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[std-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[std-kwargs1] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input4] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input5] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[std-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[size-kwargs0] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[reciprocal-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input0] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[size-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[size-kwargs1] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input1] 
[gw1] [ 91%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input2] 
[gw0] [ 91%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[size-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[nunique-kwargs0] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input3] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input3] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input4] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[nunique-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[nunique-kwargs1] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input4] 
dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input5] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc[spacing-pandas_input5] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[isreal] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[nunique-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[median-kwargs0] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[isreal] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[iscomplex] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[iscomplex] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[real] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[real] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[imag] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[median-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[median-kwargs1] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[imag] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[angle] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[angle] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[fix] 
[gw1] [ 92%] SKIPPED dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[fix] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[i0] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[i0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[sinc] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[median-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[quantile-kwargs0] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[sinc] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[nan_to_num] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped[nan_to_num] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped_not_implemented 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[quantile-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[quantile-kwargs1] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_wrapped_not_implemented 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logaddexp] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[quantile-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[ohlc-kwargs0] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[ohlc-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[ohlc-kwargs1] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logaddexp] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logaddexp2] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logaddexp2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-arctan2] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[ohlc-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[sem-kwargs0] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-arctan2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-hypot] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[sem-kwargs0] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[sem-kwargs1] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-hypot] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-copysign] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_apis[sem-kwargs1] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-30min-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-copysign] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-nextafter] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-30min-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-30min-left-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-nextafter] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-ldexp] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-30min-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-30min-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-h-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-h-right-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-ldexp] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-fmod] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-h-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-h-left-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-fmod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_and0] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-D-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-D-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-D-left-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_and0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_or0] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-D-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-W-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_or0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_xor0] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-W-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-W-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-W-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-W-left-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_xor0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-maximum] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-30min-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-maximum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-minimum] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-30min-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-30min-left-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-minimum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-fmax] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-30min-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-h-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-fmax] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-fmin] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-h-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-h-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-h-left-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-fmin] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-greater] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-h-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-D-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-greater] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-greater_equal] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-D-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-D-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-D-left-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-greater_equal] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-less] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-D-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-D-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-W-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-less] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-less_equal] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-W-right-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-less_equal] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-not_equal] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-W-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-W-left-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-not_equal] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-equal] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-count-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-30min-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-30min-right-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-equal] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_or1] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-30min-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-30min-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-30min-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-h-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_or1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_and1] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-h-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-h-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-h-left-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_and1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_xor1] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-D-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-D-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-D-left-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>0-logical_xor1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logaddexp] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-D-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-W-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-W-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-W-left-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logaddexp] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logaddexp2] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-W-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-W-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-30min-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-30min-right-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logaddexp2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-arctan2] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-30min-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-30min-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-h-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-arctan2] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-hypot] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-h-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-h-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-h-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-h-left-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-hypot] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-copysign] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-D-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-D-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-D-right-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-copysign] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-D-left-right] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-nextafter] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-D-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-D-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-W-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-nextafter] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-ldexp] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-W-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-W-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-W-left-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-ldexp] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-fmod] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-mean-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-30min-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-30min-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-30min-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-30min-left-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-fmod] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_and0] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-30min-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-h-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-h-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-h-left-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_and0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_or0] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-h-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-D-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-D-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-D-left-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_or0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_xor0] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-D-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-W-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-W-right-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_xor0] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-maximum] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-W-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-W-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-W-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-30min-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-maximum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-minimum] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-30min-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-30min-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-30min-left-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-minimum] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-fmax] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-h-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-h-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-h-right-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-fmax] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-fmin] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-h-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-h-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-D-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-fmin] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-greater] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-D-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-D-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-D-left-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-greater] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-greater_equal] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-D-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-D-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-W-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-greater_equal] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-less] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-W-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-W-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-W-left-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-less] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-less_equal] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[series-ohlc-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-30min-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-30min-right-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-less_equal] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-not_equal] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-30min-left-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-30min-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-30min-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-h-right-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-not_equal] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-equal] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-h-right-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-h-left-right] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-equal] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_or1] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_or1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_and1] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-h-left-left] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-D-right-right] 
[gw0] [ 92%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-D-right-left] 
[gw1] [ 92%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_and1] 
dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_xor1] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-D-left-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-D-left-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-W-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_ufunc_with_2args[<lambda>1-logical_xor1] 
dask/dataframe/tests/test_ufunc.py::test_clip[pandas0-5-50] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-W-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_clip[pandas0-5-50] 
dask/dataframe/tests/test_ufunc.py::test_clip[pandas1-5.5-40.5] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-W-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_clip[pandas1-5.5-40.5] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[conj] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-W-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-W-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[conj] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[exp] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-30min-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[exp] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[log] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-30min-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[log] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[log2] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[log2] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[log10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-30min-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[log10] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[log1p] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[log1p] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[expm1] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-30min-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[expm1] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[sqrt] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-h-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[sqrt] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[square] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[square] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[sin] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-h-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-h-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[sin] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[cos] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[cos] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[tan] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-h-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[tan] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arcsin] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-h-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arcsin] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arccos] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arccos] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arctan] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-D-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arctan] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[sinh] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[sinh] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[cosh] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-D-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[cosh] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[tanh] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-D-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-D-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[tanh] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arcsinh] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arcsinh] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arccosh] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-D-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arccosh] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arctanh] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[arctanh] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-D-left-left] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[deg2rad] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-W-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[deg2rad] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[rad2deg] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-W-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[rad2deg] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[isfinite] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[isfinite] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[isinf] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-W-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[isinf] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[isnan] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[isnan] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[signbit] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-W-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[signbit] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[degrees] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[degrees] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[radians] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-count-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-30min-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[radians] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[rint] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-30min-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[rint] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[fabs] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-30min-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[fabs] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[sign] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-30min-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[sign] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[absolute] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-30min-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[absolute] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[floor] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-h-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[floor] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[ceil] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-h-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[ceil] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[trunc] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-h-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[trunc] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[logical_not] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-h-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[logical_not] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[cbrt] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-D-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[cbrt] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[exp2] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-D-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[exp2] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[negative] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-D-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[negative] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[reciprocal] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[reciprocal] 
dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[spacing] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-D-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_ufunc_out[spacing] 
dask/dataframe/tests/test_ufunc.py::test_frame_2ufunc_out 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-W-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_frame_2ufunc_out 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp-2-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-W-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp-2-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-W-left-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-W-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-W-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp-arg21-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-30min-right-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-30min-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp-arg21-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-30min-left-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-30min-left-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-h-right-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-h-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp-arg21-arg11] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-h-right-left] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp2-2-arg10] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp2-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp2-2-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-h-left-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-h-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp2-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp2-arg21-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-D-right-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-D-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp2-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp2-arg21-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-D-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-D-left-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-D-left-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-D-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-W-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logaddexp2-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[arctan2-2-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-W-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[arctan2-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[arctan2-2-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-W-left-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-W-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[arctan2-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[arctan2-arg21-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-mean-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-30min-right-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-30min-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[arctan2-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[arctan2-arg21-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-30min-left-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-30min-left-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-30min-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-h-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[arctan2-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[hypot-2-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-h-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-h-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[hypot-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[hypot-2-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-h-left-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-h-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[hypot-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[hypot-arg21-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-D-right-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-D-right-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-D-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-D-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[hypot-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[hypot-arg21-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-D-left-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-D-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-W-right-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-W-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[hypot-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[copysign-2-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-W-left-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-W-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[copysign-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[copysign-2-arg11] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-W-left-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-2-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-30min-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[copysign-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[copysign-arg21-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-30min-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-30min-right-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-30min-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-30min-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[copysign-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[copysign-arg21-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-30min-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-30min-left-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-30min-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-h-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[copysign-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[nextafter-2-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-h-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-h-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[nextafter-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[nextafter-2-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-h-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-h-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[nextafter-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[nextafter-arg21-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-h-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-h-left-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-h-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-D-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[nextafter-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[nextafter-arg21-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-D-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-D-right-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-D-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-D-left-right] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-D-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-D-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[nextafter-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[ldexp-2-arg10] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[ldexp-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[ldexp-2-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-D-left-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[ldexp-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[ldexp-arg21-arg10] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-W-right-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[ldexp-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[ldexp-arg21-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-W-right-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-W-right-left] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[ldexp-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmod-2-arg10] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-W-right-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-W-left-right] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmod-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmod-2-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-W-left-right] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-W-left-left] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_series_resample[frame-ohlc-5-W-left-left] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_agg 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmod-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmod-arg21-arg10] 
[gw0] [ 93%] FAILED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_agg 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_has_correct_fill_value[count] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_has_correct_fill_value[count] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_has_correct_fill_value[nunique] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_has_correct_fill_value[nunique] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_has_correct_fill_value[size] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmod-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmod-arg21-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_has_correct_fill_value[size] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_has_correct_fill_value[sum] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_has_correct_fill_value[sum] 
dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_divisions_propagation 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmod-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and0-2-arg10] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and0-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and0-2-arg11] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and0-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and0-arg21-arg10] 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and0-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and0-arg21-arg11] 
[gw0] [ 93%] PASSED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_divisions_propagation 
dask/diagnostics/tests/test_profiler.py::test_profiler 
[gw1] [ 93%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and0-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or0-2-arg10] 
[gw0] [ 93%] PASSED dask/diagnostics/tests/test_profiler.py::test_profiler 
dask/diagnostics/tests/test_profiler.py::test_profiler_works_under_error 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_profiler.py::test_profiler_works_under_error 
dask/diagnostics/tests/test_profiler.py::test_two_gets 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_profiler.py::test_two_gets 
dask/diagnostics/tests/test_profiler.py::test_resource_profiler 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or0-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or0-2-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or0-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or0-arg21-arg10] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_profiler.py::test_resource_profiler 
dask/diagnostics/tests/test_profiler.py::test_resource_profiler_multiple_gets 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or0-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or0-arg21-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or0-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor0-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor0-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor0-2-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor0-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor0-arg21-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor0-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor0-arg21-arg11] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_profiler.py::test_resource_profiler_multiple_gets 
dask/diagnostics/tests/test_profiler.py::test_cache_profiler 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor0-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[maximum-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[maximum-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[maximum-2-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[maximum-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[maximum-arg21-arg10] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_profiler.py::test_cache_profiler 
dask/diagnostics/tests/test_profiler.py::test_register[Profiler] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[maximum-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[maximum-arg21-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[maximum-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[minimum-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[minimum-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[minimum-2-arg11] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_profiler.py::test_register[Profiler] 
dask/diagnostics/tests/test_profiler.py::test_register[<lambda>] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[minimum-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[minimum-arg21-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[minimum-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[minimum-arg21-arg11] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_profiler.py::test_register[<lambda>] 
dask/diagnostics/tests/test_profiler.py::test_register[CacheProfiler] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[minimum-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmax-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmax-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmax-2-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmax-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmax-arg21-arg10] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_profiler.py::test_register[CacheProfiler] 
dask/diagnostics/tests/test_profiler.py::test_unquote 
[gw0] [ 94%] SKIPPED dask/diagnostics/tests/test_profiler.py::test_unquote 
dask/diagnostics/tests/test_profiler.py::test_profiler_plot 
[gw0] [ 94%] SKIPPED dask/diagnostics/tests/test_profiler.py::test_profiler_plot 
dask/diagnostics/tests/test_profiler.py::test_resource_profiler_plot 
[gw0] [ 94%] SKIPPED dask/diagnostics/tests/test_profiler.py::test_resource_profiler_plot 
dask/diagnostics/tests/test_profiler.py::test_cache_profiler_plot 
[gw0] [ 94%] SKIPPED dask/diagnostics/tests/test_profiler.py::test_cache_profiler_plot 
dask/diagnostics/tests/test_profiler.py::test_cache_profiler_plot_with_invalid_bokeh_kwarg_raises_error 
[gw0] [ 94%] SKIPPED dask/diagnostics/tests/test_profiler.py::test_cache_profiler_plot_with_invalid_bokeh_kwarg_raises_error 
dask/diagnostics/tests/test_profiler.py::test_plot_multiple 
[gw0] [ 94%] SKIPPED dask/diagnostics/tests/test_profiler.py::test_plot_multiple 
dask/diagnostics/tests/test_profiler.py::test_saves_file 
[gw0] [ 94%] SKIPPED dask/diagnostics/tests/test_profiler.py::test_saves_file 
dask/diagnostics/tests/test_profiler.py::test_saves_file_path_deprecated 
[gw0] [ 94%] SKIPPED dask/diagnostics/tests/test_profiler.py::test_saves_file_path_deprecated 
dask/diagnostics/tests/test_profiler.py::test_get_colors 
[gw0] [ 94%] SKIPPED dask/diagnostics/tests/test_profiler.py::test_get_colors 
dask/diagnostics/tests/test_progress.py::test_array_compute 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmax-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmax-arg21-arg11] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_progress.py::test_array_compute 
dask/diagnostics/tests/test_progress.py::test_progressbar 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmax-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmin-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmin-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmin-2-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmin-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmin-arg21-arg10] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_progress.py::test_progressbar 
dask/diagnostics/tests/test_progress.py::test_minimum_time 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmin-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmin-arg21-arg11] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_progress.py::test_minimum_time 
dask/diagnostics/tests/test_progress.py::test_clean_exit[get] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[fmin-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater-2-arg11] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_progress.py::test_clean_exit[get] 
dask/diagnostics/tests/test_progress.py::test_clean_exit[get_sync] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater-arg21-arg10] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_progress.py::test_clean_exit[get_sync] 
dask/diagnostics/tests/test_progress.py::test_format_time 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_progress.py::test_format_time 
dask/diagnostics/tests/test_progress.py::test_register 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater-arg21-arg11] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_progress.py::test_register 
dask/diagnostics/tests/test_progress.py::test_no_tasks 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater_equal-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater_equal-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater_equal-2-arg11] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_progress.py::test_no_tasks 
dask/diagnostics/tests/test_progress.py::test_with_cache 
[gw0] [ 94%] SKIPPED dask/diagnostics/tests/test_progress.py::test_with_cache 
dask/diagnostics/tests/test_progress.py::test_with_alias 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater_equal-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater_equal-arg21-arg10] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_progress.py::test_with_alias 
dask/diagnostics/tests/test_progress.py::test_store_time 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater_equal-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater_equal-arg21-arg11] 
[gw0] [ 94%] PASSED dask/diagnostics/tests/test_progress.py::test_store_time 
dask/tests/test_backends.py::test_CreationDispatch_error_informative_message[pandas] 
[gw0] [ 94%] PASSED dask/tests/test_backends.py::test_CreationDispatch_error_informative_message[pandas] 
dask/tests/test_backends.py::test_CreationDispatch_error_informative_message[cudf] 
[gw0] [ 94%] SKIPPED dask/tests/test_backends.py::test_CreationDispatch_error_informative_message[cudf] 
dask/tests/test_base.py::test_is_dask_collection 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_is_dask_collection 
dask/tests/test_base.py::test_is_dask_collection_dask_expr 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_is_dask_collection_dask_expr 
dask/tests/test_base.py::test_is_dask_collection_dask_expr_does_not_materialize 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_is_dask_collection_dask_expr_does_not_materialize 
dask/tests/test_base.py::test_unpack_collections 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_unpack_collections 
dask/tests/test_base.py::test_get_collection_names 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_get_collection_names 
dask/tests/test_base.py::test_get_name_from_key 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_get_name_from_key 
dask/tests/test_base.py::test_replace_name_in_keys 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_replace_name_in_keys 
dask/tests/test_base.py::test_custom_collection 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_custom_collection 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[greater_equal-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[less-2-arg10] 
dask/tests/test_base.py::test_compute_no_opt 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_compute_no_opt 
dask/tests/test_base.py::test_compute_array 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_compute_array 
dask/tests/test_base.py::test_persist_array 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[less-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[less-2-arg11] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_array 
dask/tests/test_base.py::test_persist_array_rename 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_array_rename 
dask/tests/test_base.py::test_compute_dataframe 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_compute_dataframe 
dask/tests/test_base.py::test_persist_dataframe 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_dataframe 
dask/tests/test_base.py::test_persist_series 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_series 
dask/tests/test_base.py::test_persist_scalar 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[less-2-arg11] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_scalar 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[less-arg21-arg10] 
dask/tests/test_base.py::test_compute_array_dataframe 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_compute_array_dataframe 
dask/tests/test_base.py::test_compute_dataframe_valid_unicode_in_bytes 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_compute_dataframe_valid_unicode_in_bytes 
dask/tests/test_base.py::test_compute_dataframe_invalid_unicode 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_compute_dataframe_invalid_unicode 
dask/tests/test_base.py::test_compute_array_bag 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_compute_array_bag 
dask/tests/test_base.py::test_compute_with_literal 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_compute_with_literal 
dask/tests/test_base.py::test_compute_nested 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_compute_nested 
dask/tests/test_base.py::test_visualize 
[gw0] [ 94%] SKIPPED dask/tests/test_base.py::test_visualize 
dask/tests/test_base.py::test_visualize_highlevelgraph 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_visualize_highlevelgraph 
dask/tests/test_base.py::test_visualize_order 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[less-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[less-arg21-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[less-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[less_equal-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[less_equal-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[less_equal-2-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[less_equal-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[less_equal-arg21-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[less_equal-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[less_equal-arg21-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[less_equal-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[not_equal-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[not_equal-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[not_equal-2-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[not_equal-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[not_equal-arg21-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[not_equal-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[not_equal-arg21-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[not_equal-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[equal-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[equal-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[equal-2-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[equal-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[equal-arg21-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[equal-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[equal-arg21-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[equal-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or1-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or1-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or1-2-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or1-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or1-arg21-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or1-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or1-arg21-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_or1-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and1-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and1-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and1-2-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and1-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and1-arg21-arg10] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_visualize_order 
dask/tests/test_base.py::test_optimize 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_optimize 
dask/tests/test_base.py::test_optimize_nested 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_optimize_nested 
dask/tests/test_base.py::test_default_imports 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and1-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and1-arg21-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_and1-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor1-2-arg10] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor1-2-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor1-2-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor1-2-arg11] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor1-arg21-arg10] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_default_imports 
dask/tests/test_base.py::test_persist_literals 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_literals 
dask/tests/test_base.py::test_persist_nested 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_nested 
dask/tests/test_base.py::test_persist_delayed 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_delayed 
dask/tests/test_base.py::test_persist_delayed_custom_key[a] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_delayed_custom_key[a] 
dask/tests/test_base.py::test_persist_delayed_custom_key[key1] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_delayed_custom_key[key1] 
dask/tests/test_base.py::test_persist_delayed_rename[a-rename0-a] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_delayed_rename[a-rename0-a] 
dask/tests/test_base.py::test_persist_delayed_rename[a-rename1-a] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_delayed_rename[a-rename1-a] 
dask/tests/test_base.py::test_persist_delayed_rename[a-rename2-b] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_delayed_rename[a-rename2-b] 
dask/tests/test_base.py::test_persist_delayed_rename[key3-rename3-new_key3] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_delayed_rename[key3-rename3-new_key3] 
dask/tests/test_base.py::test_persist_delayedleaf 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_delayedleaf 
dask/tests/test_base.py::test_persist_delayedattr 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_delayedattr 
dask/tests/test_base.py::test_persist_array_bag 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor1-arg21-arg10] 
dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor1-arg21-arg11] 
[gw1] [ 94%] PASSED dask/dataframe/tests/test_ufunc.py::test_mixed_types[logical_xor1-arg21-arg11] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logaddexp] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logaddexp] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logaddexp2] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logaddexp2] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-arctan2] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-arctan2] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-hypot] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-hypot] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-copysign] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-copysign] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-nextafter] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-nextafter] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-ldexp] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_array_bag 
dask/tests/test_base.py::test_persist_bag 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_bag 
dask/tests/test_base.py::test_persist_item 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_item 
dask/tests/test_base.py::test_persist_bag_rename 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_bag_rename 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-ldexp] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-fmod] 
dask/tests/test_base.py::test_persist_item_change_name 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_persist_item_change_name 
dask/tests/test_base.py::test_optimize_globals 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_optimize_globals 
dask/tests/test_base.py::test_optimize_None 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_optimize_None 
dask/tests/test_base.py::test_scheduler_keyword 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_scheduler_keyword 
dask/tests/test_base.py::test_raise_get_keyword 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_raise_get_keyword 
dask/tests/test_base.py::test_get_scheduler 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_get_scheduler 
dask/tests/test_base.py::test_callable_scheduler 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_callable_scheduler 
dask/tests/test_base.py::test_num_workers_config[threads] 
[gw0] [ 94%] SKIPPED dask/tests/test_base.py::test_num_workers_config[threads] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-fmod] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_and0] 
dask/tests/test_base.py::test_num_workers_config[processes] 
[gw0] [ 94%] SKIPPED dask/tests/test_base.py::test_num_workers_config[processes] 
dask/tests/test_base.py::test_clone_key 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_clone_key 
dask/tests/test_base.py::test_compute_as_if_collection_low_level_task_graph 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_compute_as_if_collection_low_level_task_graph 
dask/tests/test_base.py::test_emscripten_default_scheduler['dask.array', 'Array', 'sync', True] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_and0] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_or0] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_or0] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_xor0] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_xor0] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-maximum] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-maximum] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-minimum] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-minimum] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-fmax] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-fmax] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-fmin] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-fmin] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-greater] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-greater] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-greater_equal] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-greater_equal] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-less] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-less] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-less_equal] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_emscripten_default_scheduler['dask.array', 'Array', 'sync', True] 
dask/tests/test_base.py::test_emscripten_default_scheduler['dask.array', 'Array', 'threads', False] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-less_equal] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-not_equal] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-not_equal] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-equal] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-equal] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_or1] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_or1] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_and1] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_and1] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_xor1] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas0-darray0-logical_xor1] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logaddexp] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logaddexp] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logaddexp2] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logaddexp2] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-arctan2] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-arctan2] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-hypot] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-hypot] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-copysign] 
[gw1] [ 94%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-copysign] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-nextafter] 
[gw0] [ 94%] PASSED dask/tests/test_base.py::test_emscripten_default_scheduler['dask.array', 'Array', 'threads', False] 
dask/tests/test_base.py::test_emscripten_default_scheduler['dask.bag', 'Bag', 'sync', True] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-nextafter] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-ldexp] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-ldexp] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-fmod] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-fmod] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_and0] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_and0] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_or0] 
[gw0] [ 95%] PASSED dask/tests/test_base.py::test_emscripten_default_scheduler['dask.bag', 'Bag', 'sync', True] 
dask/tests/test_base.py::test_emscripten_default_scheduler['dask.bag', 'Bag', 'processes', False] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_or0] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_xor0] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-logical_xor0] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-maximum] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-maximum] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-minimum] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-minimum] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-fmax] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-fmax] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-fmin] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-fmin] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-greater] 
[gw0] [ 95%] PASSED dask/tests/test_base.py::test_emscripten_default_scheduler['dask.bag', 'Bag', 'processes', False] 
dask/tests/test_callbacks.py::test_start_callback 
[gw0] [ 95%] PASSED dask/tests/test_callbacks.py::test_start_callback 
dask/tests/test_callbacks.py::test_start_state_callback 
[gw0] [ 95%] PASSED dask/tests/test_callbacks.py::test_start_state_callback 
dask/tests/test_callbacks.py::test_finish_always_called 
[gw0] [ 95%] PASSED dask/tests/test_callbacks.py::test_finish_always_called 
dask/tests/test_callbacks.py::test_nested_schedulers 
[gw0] [ 95%] PASSED dask/tests/test_callbacks.py::test_nested_schedulers 
dask/tests/test_callbacks.py::test_add_remove_mutates_not_replaces 
[gw0] [ 95%] PASSED dask/tests/test_callbacks.py::test_add_remove_mutates_not_replaces 
dask/tests/test_ci.py::test_upstream_packages_installed 
[gw0] [ 95%] SKIPPED dask/tests/test_ci.py::test_upstream_packages_installed 
dask/tests/test_cli.py::test_config_get_no_key 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_get_no_key 
dask/tests/test_cli.py::test_config_get_value 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_get_value 
dask/tests/test_cli.py::test_config_get_bad_value 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_get_bad_value 
dask/tests/test_cli.py::test_config_get_none 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_get_none 
dask/tests/test_cli.py::test_config_set_value[True-None-True-333MiB] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-None-True-333MiB] 
dask/tests/test_cli.py::test_config_set_value[True-None-True-2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-None-True-2] 
dask/tests/test_cli.py::test_config_set_value[True-None-True-value2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-None-True-value2] 
dask/tests/test_cli.py::test_config_set_value[True-None-True-value3] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-greater] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-greater_equal] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-None-True-value3] 
dask/tests/test_cli.py::test_config_set_value[True-None-True-None] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-None-True-None] 
dask/tests/test_cli.py::test_config_set_value[True-None-False-333MiB] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-None-False-333MiB] 
dask/tests/test_cli.py::test_config_set_value[True-None-False-2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-None-False-2] 
dask/tests/test_cli.py::test_config_set_value[True-None-False-value2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-None-False-value2] 
dask/tests/test_cli.py::test_config_set_value[True-None-False-value3] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-None-False-value3] 
dask/tests/test_cli.py::test_config_set_value[True-None-False-None] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-None-False-None] 
dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-True-333MiB] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-True-333MiB] 
dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-True-2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-True-2] 
dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-True-value2] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-greater_equal] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-less] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-True-value2] 
dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-True-value3] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-True-value3] 
dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-True-None] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-True-None] 
dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-False-333MiB] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-False-333MiB] 
dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-False-2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-False-2] 
dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-False-value2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-False-value2] 
dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-False-value3] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-False-value3] 
dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-False-None] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-bar.yaml-False-None] 
dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-True-333MiB] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-True-333MiB] 
dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-True-2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-True-2] 
dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-True-value2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-True-value2] 
dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-True-value3] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-less] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-less_equal] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-True-value3] 
dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-True-None] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-True-None] 
dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-False-333MiB] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-False-333MiB] 
dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-False-2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-False-2] 
dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-False-value2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-False-value2] 
dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-False-value3] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-False-value3] 
dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-False-None] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[True-foo/bar.yaml-False-None] 
dask/tests/test_cli.py::test_config_set_value[False-None-True-333MiB] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-None-True-333MiB] 
dask/tests/test_cli.py::test_config_set_value[False-None-True-2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-None-True-2] 
dask/tests/test_cli.py::test_config_set_value[False-None-True-value2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-None-True-value2] 
dask/tests/test_cli.py::test_config_set_value[False-None-True-value3] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-None-True-value3] 
dask/tests/test_cli.py::test_config_set_value[False-None-True-None] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-None-True-None] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-less_equal] 
dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-not_equal] 
dask/tests/test_cli.py::test_config_set_value[False-None-False-333MiB] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-None-False-333MiB] 
dask/tests/test_cli.py::test_config_set_value[False-None-False-2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-None-False-2] 
dask/tests/test_cli.py::test_config_set_value[False-None-False-value2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-None-False-value2] 
dask/tests/test_cli.py::test_config_set_value[False-None-False-value3] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-None-False-value3] 
dask/tests/test_cli.py::test_config_set_value[False-None-False-None] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-None-False-None] 
dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-True-333MiB] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-True-333MiB] 
dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-True-2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-True-2] 
dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-True-value2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-True-value2] 
dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-True-value3] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-True-value3] 
dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-True-None] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-True-None] 
dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-False-333MiB] 
[gw1] [ 95%] XFAIL dask/dataframe/tests/test_ufunc.py::test_2args_with_array[pandas1-darray1-not_equal] 
dask/tests/test_config.py::test_expand_environment_variables[$FOO-foo] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_expand_environment_variables[$FOO-foo] 
dask/tests/test_config.py::test_expand_environment_variables[inp3-out3] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_expand_environment_variables[inp3-out3] 
dask/tests/test_config.py::test_expand_environment_variables[inp4-out4] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_expand_environment_variables[inp4-out4] 
dask/tests/test_config.py::test_expand_environment_variables[inp5-out5] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-False-333MiB] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_expand_environment_variables[inp5-out5] 
dask/tests/test_config.py::test_expand_environment_variables[inp6-out6] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_expand_environment_variables[inp6-out6] 
dask/tests/test_config.py::test_expand_environment_variables[inp7-out7] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_expand_environment_variables[inp7-out7] 
dask/tests/test_config.py::test_env_var_canonical_name 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_env_var_canonical_name 
dask/tests/test_config.py::test_get_set_canonical_name 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_get_set_canonical_name 
dask/tests/test_config.py::test_get_set_roundtrip[custom_key] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_get_set_roundtrip[custom_key] 
dask/tests/test_config.py::test_get_set_roundtrip[custom-key] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_get_set_roundtrip[custom-key] 
dask/tests/test_config.py::test_merge_None_to_dict 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_merge_None_to_dict 
dask/tests/test_config.py::test_core_file 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_core_file 
dask/tests/test_config.py::test_schema 
dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-False-2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-False-2] 
dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-False-value2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-False-value2] 
dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-False-value3] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-False-value3] 
dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-False-None] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-bar.yaml-False-None] 
dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-True-333MiB] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-True-333MiB] 
dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-True-2] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_schema 
dask/tests/test_config.py::test_schema_is_complete 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-True-2] 
dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-True-value2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-True-value2] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_schema_is_complete 
dask/tests/test_config.py::test_rename 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_rename 
dask/tests/test_config.py::test_deprecations_on_set[args0-kwargs0] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_deprecations_on_set[args0-kwargs0] 
dask/tests/test_config.py::test_deprecations_on_set[args1-kwargs1] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_deprecations_on_set[args1-kwargs1] 
dask/tests/test_config.py::test_deprecations_on_set[args2-kwargs2] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_deprecations_on_set[args2-kwargs2] 
dask/tests/test_config.py::test_deprecations_on_env_variables 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_deprecations_on_env_variables 
dask/tests/test_config.py::test_deprecations_on_yaml[fuse-ave-width] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_deprecations_on_yaml[fuse-ave-width] 
dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-True-value3] 
dask/tests/test_config.py::test_deprecations_on_yaml[fuse_ave_width] 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_deprecations_on_yaml[fuse_ave_width] 
dask/tests/test_config.py::test_get_override_with 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_get_override_with 
dask/tests/test_config.py::test_config_serialization 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_config_serialization 
dask/tests/test_config.py::test_config_inheritance 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_config_inheritance 
dask/tests/test_config.py::test__get_paths 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test__get_paths 
dask/tests/test_config.py::test_default_search_paths 
[gw1] [ 95%] PASSED dask/tests/test_config.py::test_default_search_paths 
dask/tests/test_context.py::test_with_get 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-True-value3] 
dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-True-None] 
[gw1] [ 95%] PASSED dask/tests/test_context.py::test_with_get 
dask/tests/test_context.py::test_globalmethod 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-True-None] 
[gw1] [ 95%] PASSED dask/tests/test_context.py::test_globalmethod 
dask/tests/test_core.py::test_ishashable 
[gw1] [ 95%] PASSED dask/tests/test_core.py::test_ishashable 
dask/tests/test_core.py::test_iskey 
[gw1] [ 95%] PASSED dask/tests/test_core.py::test_iskey 
dask/tests/test_core.py::test_iskey_numpy_types 
[gw1] [ 95%] PASSED dask/tests/test_core.py::test_iskey_numpy_types 
dask/tests/test_core.py::test_validate_key 
[gw1] [ 95%] PASSED dask/tests/test_core.py::test_validate_key 
dask/tests/test_core.py::test_istask 
[gw1] [ 95%] PASSED dask/tests/test_core.py::test_istask 
dask/tests/test_core.py::test_preorder_traversal 
[gw1] [ 95%] PASSED dask/tests/test_core.py::test_preorder_traversal 
dask/tests/test_core.py::TestGet::test_get 
[gw1] [ 95%] PASSED dask/tests/test_core.py::TestGet::test_get 
dask/tests/test_core.py::TestGet::test_badkey 
dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-False-333MiB] 
[gw1] [ 95%] PASSED dask/tests/test_core.py::TestGet::test_badkey 
dask/tests/test_core.py::TestGet::test_nested_badkey 
[gw1] [ 95%] PASSED dask/tests/test_core.py::TestGet::test_nested_badkey 
dask/tests/test_core.py::TestGet::test_data_not_in_dict_is_ok 
[gw1] [ 95%] PASSED dask/tests/test_core.py::TestGet::test_data_not_in_dict_is_ok 
dask/tests/test_core.py::TestGet::test_get_with_list 
[gw1] [ 95%] PASSED dask/tests/test_core.py::TestGet::test_get_with_list 
dask/tests/test_core.py::TestGet::test_get_with_list_top_level 
[gw1] [ 95%] PASSED dask/tests/test_core.py::TestGet::test_get_with_list_top_level 
dask/tests/test_core.py::TestGet::test_get_with_nested_list 
[gw1] [ 95%] PASSED dask/tests/test_core.py::TestGet::test_get_with_nested_list 
dask/tests/test_core.py::TestGet::test_get_works_with_unhashables_in_values 
[gw1] [ 95%] PASSED dask/tests/test_core.py::TestGet::test_get_works_with_unhashables_in_values 
dask/tests/test_core.py::TestGet::test_nested_tasks 
[gw1] [ 95%] PASSED dask/tests/test_core.py::TestGet::test_nested_tasks 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-False-333MiB] 
dask/tests/test_core.py::TestGet::test_get_stack_limit 
dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-False-2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-False-2] 
dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-False-value2] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-False-value2] 
dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-False-value3] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-False-value3] 
dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-False-None] 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_set_value[False-foo/bar.yaml-False-None] 
dask/tests/test_cli.py::test_config_find 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_find 
dask/tests/test_cli.py::test_config_list 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_config_list 
dask/tests/test_cli.py::test_version 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_version 
dask/tests/test_cli.py::test_info_versions 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_info_versions 
dask/tests/test_cli.py::test_register_command_ep 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_register_command_ep 
dask/tests/test_cli.py::test_repeated_name_registration_warn 
[gw0] [ 95%] PASSED dask/tests/test_cli.py::test_repeated_name_registration_warn 
dask/tests/test_compatibility.py::test_entry_points 
[gw0] [ 95%] PASSED dask/tests/test_compatibility.py::test_entry_points 
dask/tests/test_config.py::test_canonical_name 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_canonical_name 
dask/tests/test_config.py::test_update 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_update 
dask/tests/test_config.py::test_update_new_defaults 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_update_new_defaults 
dask/tests/test_config.py::test_update_defaults 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_update_defaults 
dask/tests/test_config.py::test_update_list_to_dict 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_update_list_to_dict 
dask/tests/test_config.py::test_update_dict_to_list 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_update_dict_to_list 
dask/tests/test_config.py::test_merge 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_merge 
dask/tests/test_config.py::test_collect_yaml_paths 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_collect_yaml_paths 
dask/tests/test_config.py::test_paths_containing_key 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_paths_containing_key 
dask/tests/test_config.py::test_collect_yaml_dir 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_collect_yaml_dir 
dask/tests/test_config.py::test_collect_yaml_permission_errors[directory] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_collect_yaml_permission_errors[directory] 
dask/tests/test_config.py::test_collect_yaml_permission_errors[file] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_collect_yaml_permission_errors[file] 
dask/tests/test_config.py::test_collect_yaml_malformed_file 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_collect_yaml_malformed_file 
dask/tests/test_config.py::test_collect_yaml_no_top_level_dict 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_collect_yaml_no_top_level_dict 
dask/tests/test_config.py::test_env 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env 
dask/tests/test_config.py::test_env_special_values[None-None-<lambda>0] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[None-None-<lambda>0] 
dask/tests/test_config.py::test_env_special_values[None-None-<lambda>1] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[None-None-<lambda>1] 
dask/tests/test_config.py::test_env_special_values[None-None-<lambda>2] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[None-None-<lambda>2] 
dask/tests/test_config.py::test_env_special_values[Null-None-<lambda>0] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[Null-None-<lambda>0] 
dask/tests/test_config.py::test_env_special_values[Null-None-<lambda>1] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[Null-None-<lambda>1] 
dask/tests/test_config.py::test_env_special_values[Null-None-<lambda>2] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[Null-None-<lambda>2] 
dask/tests/test_config.py::test_env_special_values[False-False-<lambda>0] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[False-False-<lambda>0] 
dask/tests/test_config.py::test_env_special_values[False-False-<lambda>1] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[False-False-<lambda>1] 
dask/tests/test_config.py::test_env_special_values[False-False-<lambda>2] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[False-False-<lambda>2] 
dask/tests/test_config.py::test_env_special_values[True-True-<lambda>0] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[True-True-<lambda>0] 
dask/tests/test_config.py::test_env_special_values[True-True-<lambda>1] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[True-True-<lambda>1] 
dask/tests/test_config.py::test_env_special_values[True-True-<lambda>2] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_env_special_values[True-True-<lambda>2] 
dask/tests/test_config.py::test_collect 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_collect 
dask/tests/test_config.py::test_collect_env_none 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_collect_env_none 
dask/tests/test_config.py::test_get 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_get 
dask/tests/test_config.py::test_ensure_file 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_ensure_file 
dask/tests/test_config.py::test_set 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_set 
dask/tests/test_config.py::test_set_kwargs 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_set_kwargs 
dask/tests/test_config.py::test_set_nested 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_set_nested 
dask/tests/test_config.py::test_set_hard_to_copyables 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_set_hard_to_copyables 
dask/tests/test_config.py::test_ensure_file_directory[True] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_ensure_file_directory[True] 
dask/tests/test_config.py::test_ensure_file_directory[False] 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_ensure_file_directory[False] 
dask/tests/test_config.py::test_ensure_file_defaults_to_DASK_CONFIG_directory 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_ensure_file_defaults_to_DASK_CONFIG_directory 
dask/tests/test_config.py::test_pop 
[gw0] [ 95%] PASSED dask/tests/test_config.py::test_pop 
dask/tests/test_config.py::test_refresh 
[gw0] [ 96%] PASSED dask/tests/test_config.py::test_refresh 
dask/tests/test_config.py::test_expand_environment_variables[1-1_0] 
[gw0] [ 96%] PASSED dask/tests/test_config.py::test_expand_environment_variables[1-1_0] 
dask/tests/test_config.py::test_expand_environment_variables[1-1_1] 
[gw0] [ 96%] PASSED dask/tests/test_config.py::test_expand_environment_variables[1-1_1] 
dask/tests/test_delayed.py::test_pure_global_setting 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_pure_global_setting 
dask/tests/test_delayed.py::test_nout 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_nout 
dask/tests/test_delayed.py::test_nout_with_tasks[x0] 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_nout_with_tasks[x0] 
dask/tests/test_delayed.py::test_nout_with_tasks[x1] 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_nout_with_tasks[x1] 
dask/tests/test_delayed.py::test_nout_with_tasks[x2] 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_nout_with_tasks[x2] 
dask/tests/test_delayed.py::test_nout_with_tasks[x3] 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_nout_with_tasks[x3] 
dask/tests/test_delayed.py::test_nout_with_tasks[x4] 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_nout_with_tasks[x4] 
dask/tests/test_delayed.py::test_kwargs 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_kwargs 
dask/tests/test_delayed.py::test_custom_delayed 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_custom_delayed 
dask/tests/test_delayed.py::test_array_delayed 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_array_delayed 
dask/tests/test_delayed.py::test_array_delayed_complex_optimization 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_array_delayed_complex_optimization 
dask/tests/test_delayed.py::test_array_delayed_complex_optimization_kwargs 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_array_delayed_complex_optimization_kwargs 
dask/tests/test_delayed.py::test_array_bag_delayed 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_array_bag_delayed 
dask/tests/test_delayed.py::test_delayed_picklable 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_picklable 
dask/tests/test_delayed.py::test_delayed_compute_forward_kwargs 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_compute_forward_kwargs 
dask/tests/test_delayed.py::test_delayed_method_descriptor 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_method_descriptor 
dask/tests/test_delayed.py::test_delayed_callable 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_callable 
dask/tests/test_delayed.py::test_delayed_name_on_call 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_name_on_call 
dask/tests/test_delayed.py::test_callable_obj 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_callable_obj 
dask/tests/test_delayed.py::test_deterministic_name 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_deterministic_name 
dask/tests/test_delayed.py::test_sensitive_to_partials 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_sensitive_to_partials 
dask/tests/test_delayed.py::test_delayed_name 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_name 
dask/tests/test_delayed.py::test_finalize_name 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_finalize_name 
dask/tests/test_delayed.py::test_keys_from_array 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_keys_from_array 
dask/tests/test_delayed.py::test_delayed_decorator_on_method 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_decorator_on_method 
dask/tests/test_delayed.py::test_attribute_of_attribute 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_attribute_of_attribute 
dask/tests/test_delayed.py::test_check_meta_flag 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_check_meta_flag 
dask/tests/test_delayed.py::test_pickle[modlevel_eager] 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_pickle[modlevel_eager] 
dask/tests/test_delayed.py::test_pickle[modlevel_delayed1] 
[gw0] [ 96%] XFAIL dask/tests/test_delayed.py::test_pickle[modlevel_delayed1] 
dask/tests/test_delayed.py::test_pickle[modlevel_delayed2] 
[gw0] [ 96%] XFAIL dask/tests/test_delayed.py::test_pickle[modlevel_delayed2] 
dask/tests/test_delayed.py::test_cloudpickle[modlevel_eager] 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_cloudpickle[modlevel_eager] 
dask/tests/test_delayed.py::test_cloudpickle[modlevel_delayed1] 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_cloudpickle[modlevel_delayed1] 
dask/tests/test_delayed.py::test_cloudpickle[modlevel_delayed2] 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_cloudpickle[modlevel_delayed2] 
dask/tests/test_delayed.py::test_dask_layers 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_dask_layers 
dask/tests/test_delayed.py::test_annotations_survive_optimization 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_annotations_survive_optimization 
dask/tests/test_delayed.py::test_delayed_function_attributes_forwarded 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_function_attributes_forwarded 
dask/tests/test_delayed.py::test_delayed_fusion 
[gw0] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_fusion 
dask/tests/test_delayed.py::test_p2p_as_delayed 
[gw0] [ 96%] SKIPPED dask/tests/test_delayed.py::test_p2p_as_delayed 
dask/tests/test_expr.py::test_setattr 
[gw0] [ 96%] PASSED dask/tests/test_expr.py::test_setattr 
dask/tests/test_expr.py::test_setattr2 
[gw0] [ 96%] PASSED dask/tests/test_expr.py::test_setattr2 
dask/tests/test_expr.py::test_pickle_cached_properties 
[gw0] [ 96%] SKIPPED dask/tests/test_expr.py::test_pickle_cached_properties 
dask/tests/test_expr.py::test_singleton_expr 
[gw0] [ 96%] PASSED dask/tests/test_expr.py::test_singleton_expr 
dask/tests/test_expr.py::test_refcounting_futures 
[gw0] [ 96%] SKIPPED dask/tests/test_expr.py::test_refcounting_futures 
dask/tests/test_expr.py::test_prohibit_reuse 
[gw1] [ 96%] PASSED dask/tests/test_core.py::TestGet::test_get_stack_limit 
[gw0] [ 96%] PASSED dask/tests/test_expr.py::test_prohibit_reuse 
dask/tests/test_core.py::TestGet::test_with_HighLevelGraph 
dask/tests/test_graph_manipulation.py::test_checkpoint[False] 
[gw1] [ 96%] PASSED dask/tests/test_core.py::TestGet::test_with_HighLevelGraph 
dask/tests/test_core.py::test_GetFunctionTestMixin_class 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_GetFunctionTestMixin_class 
dask/tests/test_core.py::test_get_dependencies_nested 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_get_dependencies_nested 
dask/tests/test_core.py::test_get_dependencies_empty 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_get_dependencies_empty 
dask/tests/test_core.py::test_get_dependencies_list 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_get_dependencies_list 
dask/tests/test_core.py::test_get_dependencies_task 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_get_dependencies_task 
dask/tests/test_core.py::test_get_dependencies_nothing 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_get_dependencies_nothing 
dask/tests/test_core.py::test_get_dependencies_many 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_get_dependencies_many 
dask/tests/test_core.py::test_get_dependencies_task_none 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_get_dependencies_task_none 
dask/tests/test_core.py::test_get_deps 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_get_deps 
dask/tests/test_core.py::test_flatten 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_flatten 
dask/tests/test_core.py::test_subs 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_subs 
dask/tests/test_core.py::test_subs_no_key_data_eq 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_subs_no_key_data_eq 
dask/tests/test_core.py::test_subs_with_unfriendly_eq 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_subs_with_unfriendly_eq 
dask/tests/test_core.py::test_subs_with_surprisingly_friendly_eq 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_checkpoint[False] 
dask/tests/test_graph_manipulation.py::test_checkpoint[True] 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_subs_with_surprisingly_friendly_eq 
dask/tests/test_core.py::test_subs_arbitrary_key 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_subs_arbitrary_key 
dask/tests/test_core.py::test_quote 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_quote 
dask/tests/test_core.py::test_literal_serializable 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_literal_serializable 
dask/tests/test_core.py::test_getcycle 
[gw1] [ 96%] PASSED dask/tests/test_core.py::test_getcycle 
dask/tests/test_datasets.py::test_mimesis 
[gw1] [ 96%] SKIPPED dask/tests/test_datasets.py::test_mimesis 
dask/tests/test_datasets.py::test_full_dataset 
[gw1] [ 96%] SKIPPED dask/tests/test_datasets.py::test_full_dataset 
dask/tests/test_datasets.py::test_make_dataset_with_processes 
[gw1] [ 96%] SKIPPED dask/tests/test_datasets.py::test_make_dataset_with_processes 
dask/tests/test_datasets.py::test_no_mimesis 
[gw1] [ 96%] PASSED dask/tests/test_datasets.py::test_no_mimesis 
dask/tests/test_datasets.py::test_deterministic 
[gw1] [ 96%] SKIPPED dask/tests/test_datasets.py::test_deterministic 
dask/tests/test_delayed.py::test_to_task_dask 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_to_task_dask 
dask/tests/test_delayed.py::test_delayed 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed 
dask/tests/test_delayed.py::test_delayed_with_namedtuple 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_checkpoint[True] 
dask/tests/test_graph_manipulation.py::test_checkpoint_collections 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_with_namedtuple 
dask/tests/test_delayed.py::test_delayed_with_dataclass[ANonFrozenDataClass] 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_with_dataclass[ANonFrozenDataClass] 
dask/tests/test_delayed.py::test_delayed_with_dataclass[AFrozenDataClass] 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_with_dataclass[AFrozenDataClass] 
dask/tests/test_delayed.py::test_delayed_with_dataclass_with_custom_init 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_with_dataclass_with_custom_init 
dask/tests/test_delayed.py::test_delayed_with_dataclass_with_eager_custom_init 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_with_dataclass_with_eager_custom_init 
dask/tests/test_delayed.py::test_delayed_with_eager_dataclass_with_set_init_false_field 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_with_eager_dataclass_with_set_init_false_field 
dask/tests/test_delayed.py::test_delayed_with_dataclass_with_set_init_false_field 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_with_dataclass_with_set_init_false_field 
dask/tests/test_delayed.py::test_delayed_with_dataclass_with_unset_init_false_field 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_with_dataclass_with_unset_init_false_field 
dask/tests/test_delayed.py::test_operators 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_operators 
dask/tests/test_delayed.py::test_methods 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_methods 
dask/tests/test_delayed.py::test_attributes 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_attributes 
dask/tests/test_delayed.py::test_method_getattr_call_same_task 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_method_getattr_call_same_task 
dask/tests/test_delayed.py::test_np_dtype_of_delayed 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_np_dtype_of_delayed 
dask/tests/test_delayed.py::test_delayed_visualise_warn 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_visualise_warn 
dask/tests/test_delayed.py::test_delayed_errors 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_delayed_errors 
dask/tests/test_delayed.py::test_common_subexpressions 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_common_subexpressions 
dask/tests/test_delayed.py::test_lists 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_lists 
dask/tests/test_delayed.py::test_literates 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_literates 
dask/tests/test_delayed.py::test_literates_keys 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_literates_keys 
dask/tests/test_delayed.py::test_lists_are_concrete 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_lists_are_concrete 
dask/tests/test_delayed.py::test_iterators[list] 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_iterators[list] 
dask/tests/test_delayed.py::test_iterators[tuple] 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_iterators[tuple] 
dask/tests/test_delayed.py::test_iterators[set] 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_iterators[set] 
dask/tests/test_delayed.py::test_traverse_false 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_traverse_false 
dask/tests/test_delayed.py::test_pure 
[gw1] [ 96%] PASSED dask/tests/test_delayed.py::test_pure 
dask/tests/test_hashing.py::test_hash_buffer_hex[x6] 
[gw1] [ 96%] PASSED dask/tests/test_hashing.py::test_hash_buffer_hex[x6] 
dask/tests/test_hashing.py::test_hashers[_hash_sha1] 
[gw1] [ 96%] PASSED dask/tests/test_hashing.py::test_hashers[_hash_sha1] 
dask/tests/test_highgraph.py::test_visualize 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_visualize 
dask/tests/test_highgraph.py::test_basic 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_basic 
dask/tests/test_highgraph.py::test_keys_values_items_to_dict_methods 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_keys_values_items_to_dict_methods 
dask/tests/test_highgraph.py::test_getitem 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_getitem 
dask/tests/test_highgraph.py::test_copy 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_copy 
dask/tests/test_highgraph.py::test_cull 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_cull 
dask/tests/test_highgraph.py::test_cull_layers 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_cull_layers 
dask/tests/test_highgraph.py::test_repr_html_hlg_layers 
[gw1] [ 96%] SKIPPED dask/tests/test_highgraph.py::test_repr_html_hlg_layers 
dask/tests/test_highgraph.py::test_single_annotation[annotation0] 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_single_annotation[annotation0] 
dask/tests/test_highgraph.py::test_single_annotation[annotation1] 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_single_annotation[annotation1] 
dask/tests/test_highgraph.py::test_multiple_annotations 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_multiple_annotations 
dask/tests/test_highgraph.py::test_annotation_cleared_on_error 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_annotation_cleared_on_error 
dask/tests/test_highgraph.py::test_materializedlayer_cull_preserves_annotations 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_materializedlayer_cull_preserves_annotations 
dask/tests/test_highgraph.py::test_annotations_leak 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_annotations_leak 
dask/tests/test_highgraph.py::test_blockwise_cull[True] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_checkpoint_collections 
dask/tests/test_graph_manipulation.py::test_wait_on_one[False] 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_blockwise_cull[True] 
dask/tests/test_highgraph.py::test_blockwise_cull[False] 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_blockwise_cull[False] 
dask/tests/test_highgraph.py::test_len_does_not_materialize 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_len_does_not_materialize 
dask/tests/test_highgraph.py::test_node_tooltips_exist 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_node_tooltips_exist 
dask/tests/test_highgraph.py::test_tokenize_hlg 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_tokenize_hlg 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_wait_on_one[False] 
dask/tests/test_highgraph.py::test_culling_changes_layer_names 
[gw1] [ 96%] PASSED dask/tests/test_highgraph.py::test_culling_changes_layer_names 
dask/tests/test_graph_manipulation.py::test_wait_on_one[True] 
dask/tests/test_hlgexpr.py::test_tokenize 
[gw1] [ 96%] PASSED dask/tests/test_hlgexpr.py::test_tokenize 
dask/tests/test_hlgexpr.py::test_hlg_expr_sequence_finalize 
[gw1] [ 96%] PASSED dask/tests/test_hlgexpr.py::test_hlg_expr_sequence_finalize 
dask/tests/test_hlgexpr.py::test_hlg_expr_sequence_nested_keys 
[gw1] [ 96%] PASSED dask/tests/test_hlgexpr.py::test_hlg_expr_sequence_nested_keys 
dask/tests/test_hlgexpr.py::test_hlg_sequence_uses_annotations_of_optimized_dsk 
[gw1] [ 96%] PASSED dask/tests/test_hlgexpr.py::test_hlg_sequence_uses_annotations_of_optimized_dsk 
dask/tests/test_hlgexpr.py::test_hlg_blockwise_fusion 
[gw1] [ 96%] PASSED dask/tests/test_hlgexpr.py::test_hlg_blockwise_fusion 
dask/tests/test_imports.py::test_defaults[dask] 
[gw1] [ 96%] PASSED dask/tests/test_imports.py::test_defaults[dask] 
dask/tests/test_imports.py::test_defaults[dask.bag] 
[gw1] [ 96%] PASSED dask/tests/test_imports.py::test_defaults[dask.bag] 
dask/tests/test_imports.py::test_defaults[dask.base] 
[gw1] [ 96%] PASSED dask/tests/test_imports.py::test_defaults[dask.base] 
dask/tests/test_imports.py::test_defaults[dask.delayed] 
[gw1] [ 96%] PASSED dask/tests/test_imports.py::test_defaults[dask.delayed] 
dask/tests/test_imports.py::test_defaults[dask.graph_manipulation] 
[gw1] [ 96%] PASSED dask/tests/test_imports.py::test_defaults[dask.graph_manipulation] 
dask/tests/test_imports.py::test_defaults[dask.layers] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_wait_on_one[True] 
dask/tests/test_graph_manipulation.py::test_wait_on_many[False] 
[gw1] [ 96%] PASSED dask/tests/test_imports.py::test_defaults[dask.layers] 
dask/tests/test_imports.py::test_defaults[dask.multiprocessing] 
[gw1] [ 96%] PASSED dask/tests/test_imports.py::test_defaults[dask.multiprocessing] 
dask/tests/test_imports.py::test_defaults[dask.optimization] 
[gw1] [ 96%] PASSED dask/tests/test_imports.py::test_defaults[dask.optimization] 
dask/tests/test_imports.py::test_defaults[dask.threaded] 
[gw1] [ 96%] PASSED dask/tests/test_imports.py::test_defaults[dask.threaded] 
dask/tests/test_imports.py::test_array 
[gw1] [ 96%] PASSED dask/tests/test_imports.py::test_array 
dask/tests/test_imports.py::test_pandas_pyarrow 
[gw1] [ 96%] PASSED dask/tests/test_imports.py::test_pandas_pyarrow 
dask/tests/test_imports.py::test_bokeh 
[gw1] [ 96%] SKIPPED dask/tests/test_imports.py::test_bokeh 
dask/tests/test_imports.py::test_distributed 
[gw1] [ 96%] SKIPPED dask/tests/test_imports.py::test_distributed 
dask/tests/test_local.py::test_start_state 
[gw1] [ 96%] PASSED dask/tests/test_local.py::test_start_state 
dask/tests/test_local.py::test_start_state_looks_at_cache 
[gw1] [ 96%] PASSED dask/tests/test_local.py::test_start_state_looks_at_cache 
dask/tests/test_local.py::test_start_state_with_redirects 
[gw1] [ 96%] PASSED dask/tests/test_local.py::test_start_state_with_redirects 
dask/tests/test_local.py::test_start_state_with_independent_but_runnable_tasks 
[gw1] [ 96%] PASSED dask/tests/test_local.py::test_start_state_with_independent_but_runnable_tasks 
dask/tests/test_local.py::test_start_state_with_tasks_no_deps 
[gw1] [ 96%] PASSED dask/tests/test_local.py::test_start_state_with_tasks_no_deps 
dask/tests/test_local.py::test_finish_task 
[gw1] [ 96%] PASSED dask/tests/test_local.py::test_finish_task 
dask/tests/test_local.py::TestGetAsync::test_get 
[gw1] [ 96%] PASSED dask/tests/test_local.py::TestGetAsync::test_get 
dask/tests/test_local.py::TestGetAsync::test_badkey 
[gw1] [ 96%] PASSED dask/tests/test_local.py::TestGetAsync::test_badkey 
dask/tests/test_local.py::TestGetAsync::test_nested_badkey 
[gw1] [ 96%] PASSED dask/tests/test_local.py::TestGetAsync::test_nested_badkey 
dask/tests/test_local.py::TestGetAsync::test_data_not_in_dict_is_ok 
[gw1] [ 96%] PASSED dask/tests/test_local.py::TestGetAsync::test_data_not_in_dict_is_ok 
dask/tests/test_local.py::TestGetAsync::test_get_with_list 
[gw1] [ 96%] PASSED dask/tests/test_local.py::TestGetAsync::test_get_with_list 
dask/tests/test_local.py::TestGetAsync::test_get_with_list_top_level 
[gw1] [ 96%] PASSED dask/tests/test_local.py::TestGetAsync::test_get_with_list_top_level 
dask/tests/test_local.py::TestGetAsync::test_get_with_nested_list 
[gw1] [ 96%] PASSED dask/tests/test_local.py::TestGetAsync::test_get_with_nested_list 
dask/tests/test_local.py::TestGetAsync::test_get_works_with_unhashables_in_values 
[gw1] [ 96%] PASSED dask/tests/test_local.py::TestGetAsync::test_get_works_with_unhashables_in_values 
dask/tests/test_local.py::TestGetAsync::test_nested_tasks 
[gw1] [ 96%] PASSED dask/tests/test_local.py::TestGetAsync::test_nested_tasks 
dask/tests/test_local.py::TestGetAsync::test_get_stack_limit 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_wait_on_many[False] 
dask/tests/test_graph_manipulation.py::test_wait_on_many[True] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_wait_on_many[True] 
dask/tests/test_graph_manipulation.py::test_clone[False] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_clone[False] 
dask/tests/test_graph_manipulation.py::test_clone[True] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_clone[True] 
dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_literals[1] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_literals[1] 
dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_literals[literal1] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_literals[literal1] 
dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_literals[literal2] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_literals[literal2] 
dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_literals[literal3] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_literals[literal3] 
dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_literals[literal4] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_literals[literal4] 
dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_no_indices 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_blockwise_clone_with_no_indices 
dask/tests/test_graph_manipulation.py::test_bind[False] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_bind[False] 
dask/tests/test_graph_manipulation.py::test_bind[True] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_bind[True] 
dask/tests/test_graph_manipulation.py::test_split_every[2-299] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_split_every[2-299] 
dask/tests/test_graph_manipulation.py::test_split_every[3-250] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_split_every[3-250] 
dask/tests/test_graph_manipulation.py::test_split_every[8-215] 
[gw0] [ 96%] PASSED dask/tests/test_graph_manipulation.py::test_split_every[8-215] 
dask/tests/test_graph_manipulation.py::test_split_every[None-215] 
[gw1] [ 96%] PASSED dask/tests/test_local.py::TestGetAsync::test_get_stack_limit 
dask/tests/test_local.py::TestGetAsync::test_with_HighLevelGraph 
[gw1] [ 96%] PASSED dask/tests/test_local.py::TestGetAsync::test_with_HighLevelGraph 
[gw0] [ 97%] PASSED dask/tests/test_graph_manipulation.py::test_split_every[None-215] 
dask/tests/test_graph_manipulation.py::test_split_every[8.1-215] 
dask/tests/test_local.py::TestGetAsync::test_get_sync_num_workers 
[gw1] [ 97%] PASSED dask/tests/test_local.py::TestGetAsync::test_get_sync_num_workers 
dask/tests/test_local.py::test_cache_options 
[gw1] [ 97%] PASSED dask/tests/test_local.py::test_cache_options 
dask/tests/test_local.py::test_sort_key 
[gw1] [ 97%] PASSED dask/tests/test_local.py::test_sort_key 
dask/tests/test_local.py::test_callback 
[gw1] [ 97%] PASSED dask/tests/test_local.py::test_callback 
dask/tests/test_local.py::test_exceptions_propagate 
[gw1] [ 97%] PASSED dask/tests/test_local.py::test_exceptions_propagate 
dask/tests/test_local.py::test_ordering 
[gw1] [ 97%] PASSED dask/tests/test_local.py::test_ordering 
dask/tests/test_local.py::test_complex_ordering 
[gw1] [ 97%] PASSED dask/tests/test_local.py::test_complex_ordering 
dask/tests/test_local.py::test_ensure_calculate_only_whats_needed 
[gw1] [ 97%] PASSED dask/tests/test_local.py::test_ensure_calculate_only_whats_needed 
dask/tests/test_ml.py::test_basic 
[gw1] [ 97%] PASSED dask/tests/test_ml.py::test_basic 
dask/tests/test_multiprocessing.py::test_pickle_globals 
[gw1] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_pickle_globals 
dask/tests/test_multiprocessing.py::test_pickle_locals 
[gw1] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_pickle_locals 
dask/tests/test_multiprocessing.py::test_out_of_band_pickling 
[gw1] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_out_of_band_pickling 
dask/tests/test_multiprocessing.py::test_errors_propagate 
[gw0] [ 97%] PASSED dask/tests/test_graph_manipulation.py::test_split_every[8.1-215] 
dask/tests/test_graph_manipulation.py::test_split_every[1000000000.0-201] 
[gw0] [ 97%] PASSED dask/tests/test_graph_manipulation.py::test_split_every[1000000000.0-201] 
dask/tests/test_graph_manipulation.py::test_split_every[False-201] 
[gw0] [ 97%] PASSED dask/tests/test_graph_manipulation.py::test_split_every[False-201] 
dask/tests/test_graph_manipulation.py::test_split_every_invalid 
[gw0] [ 97%] PASSED dask/tests/test_graph_manipulation.py::test_split_every_invalid 
dask/tests/test_hashing.py::test_hash_buffer[abc] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer[abc] 
dask/tests/test_hashing.py::test_hash_buffer[x1] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer[x1] 
dask/tests/test_hashing.py::test_hash_buffer[x2] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer[x2] 
dask/tests/test_hashing.py::test_hash_buffer[x3] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer[x3] 
dask/tests/test_hashing.py::test_hash_buffer[x4] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer[x4] 
dask/tests/test_hashing.py::test_hash_buffer[x5] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer[x5] 
dask/tests/test_hashing.py::test_hash_buffer[x6] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer[x6] 
dask/tests/test_hashing.py::test_hash_buffer_hex[abc] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer_hex[abc] 
dask/tests/test_hashing.py::test_hash_buffer_hex[x1] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer_hex[x1] 
dask/tests/test_hashing.py::test_hash_buffer_hex[x2] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer_hex[x2] 
dask/tests/test_hashing.py::test_hash_buffer_hex[x3] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer_hex[x3] 
dask/tests/test_hashing.py::test_hash_buffer_hex[x4] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer_hex[x4] 
dask/tests/test_hashing.py::test_hash_buffer_hex[x5] 
[gw0] [ 97%] PASSED dask/tests/test_hashing.py::test_hash_buffer_hex[x5] 
dask/tests/test_multiprocessing.py::test_lambda_results_with_cloudpickle 
[gw1] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_errors_propagate 
dask/tests/test_multiprocessing.py::test_remote_exception 
[gw1] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_remote_exception 
dask/tests/test_multiprocessing.py::test_lambda_with_cloudpickle 
[gw0] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_lambda_results_with_cloudpickle 
dask/tests/test_multiprocessing.py::test_unpicklable_args_generate_errors 
[gw1] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_lambda_with_cloudpickle 
dask/tests/test_order.py::test_gh_3055 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_gh_3055 
dask/tests/test_order.py::test_type_comparisions_ok[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_type_comparisions_ok[abcde] 
dask/tests/test_order.py::test_type_comparisions_ok[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_type_comparisions_ok[edcba] 
dask/tests/test_order.py::test_favor_longest_critical_path[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_favor_longest_critical_path[abcde] 
dask/tests/test_order.py::test_favor_longest_critical_path[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_favor_longest_critical_path[edcba] 
dask/tests/test_order.py::test_run_smaller_sections[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_run_smaller_sections[abcde] 
dask/tests/test_order.py::test_run_smaller_sections[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_run_smaller_sections[edcba] 
dask/tests/test_order.py::test_local_parents_of_reduction[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_local_parents_of_reduction[abcde] 
dask/tests/test_order.py::test_local_parents_of_reduction[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_local_parents_of_reduction[edcba] 
dask/tests/test_order.py::test_nearest_neighbor[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_nearest_neighbor[abcde] 
dask/tests/test_order.py::test_nearest_neighbor[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_nearest_neighbor[edcba] 
dask/tests/test_order.py::test_string_ordering 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_string_ordering 
dask/tests/test_order.py::test_string_ordering_dependents 
[gw1] [ 97%] XPASS dask/tests/test_order.py::test_string_ordering_dependents 
dask/tests/test_order.py::test_prefer_short_narrow[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_prefer_short_narrow[abcde] 
dask/tests/test_order.py::test_prefer_short_narrow[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_prefer_short_narrow[edcba] 
dask/tests/test_order.py::test_prefer_short_ancestor[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_prefer_short_ancestor[abcde] 
dask/tests/test_order.py::test_prefer_short_ancestor[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_prefer_short_ancestor[edcba] 
dask/tests/test_order.py::test_map_overlap[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_map_overlap[abcde] 
dask/tests/test_order.py::test_map_overlap[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_map_overlap[edcba] 
dask/tests/test_order.py::test_use_structure_not_keys[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_use_structure_not_keys[abcde] 
dask/tests/test_order.py::test_use_structure_not_keys[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_use_structure_not_keys[edcba] 
dask/tests/test_order.py::test_dont_run_all_dependents_too_early[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_dont_run_all_dependents_too_early[abcde] 
dask/tests/test_order.py::test_dont_run_all_dependents_too_early[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_dont_run_all_dependents_too_early[edcba] 
dask/tests/test_order.py::test_many_branches_use_ndependencies[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_many_branches_use_ndependencies[abcde] 
dask/tests/test_order.py::test_many_branches_use_ndependencies[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_many_branches_use_ndependencies[edcba] 
dask/tests/test_order.py::test_order_cycle 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_order_cycle 
dask/tests/test_order.py::test_order_empty 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_order_empty 
dask/tests/test_order.py::test_order_with_equal_dependents[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_order_with_equal_dependents[abcde] 
dask/tests/test_order.py::test_order_with_equal_dependents[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_order_with_equal_dependents[edcba] 
dask/tests/test_order.py::test_terminal_node_backtrack 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_terminal_node_backtrack 
dask/tests/test_order.py::test_array_store_final_order 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_array_store_final_order 
dask/tests/test_order.py::test_eager_to_compute_dependent_to_free_parent 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_eager_to_compute_dependent_to_free_parent 
dask/tests/test_order.py::test_diagnostics[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_diagnostics[abcde] 
dask/tests/test_order.py::test_diagnostics[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_diagnostics[edcba] 
dask/tests/test_order.py::test_xarray_like_reduction 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_xarray_like_reduction 
dask/tests/test_order.py::test_array_vs_dataframe[True] 
[gw1] [ 97%] SKIPPED dask/tests/test_order.py::test_array_vs_dataframe[True] 
dask/tests/test_order.py::test_array_vs_dataframe[False] 
[gw1] [ 97%] SKIPPED dask/tests/test_order.py::test_array_vs_dataframe[False] 
dask/tests/test_order.py::test_anom_mean 
[gw1] [ 97%] SKIPPED dask/tests/test_order.py::test_anom_mean 
dask/tests/test_order.py::test_anom_mean_raw[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_anom_mean_raw[abcde] 
dask/tests/test_order.py::test_anom_mean_raw[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_anom_mean_raw[edcba] 
dask/tests/test_order.py::test_flaky_array_reduction 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_flaky_array_reduction 
dask/tests/test_order.py::test_flox_reduction[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_flox_reduction[abcde] 
dask/tests/test_order.py::test_flox_reduction[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_flox_reduction[edcba] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-2-True-True] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-2-True-True] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-2-True-False] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-2-True-False] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-2-False-True] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-2-False-True] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-2-False-False] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-2-False-False] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-5-True-True] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-5-True-True] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-5-True-False] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-5-True-False] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-5-False-True] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-5-False-True] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-5-False-False] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[4-5-False-False] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-2-True-True] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-2-True-True] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-2-True-False] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-2-True-False] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-2-False-True] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-2-False-True] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-2-False-False] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-2-False-False] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-5-True-True] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-5-True-True] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-5-True-False] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-5-True-False] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-5-False-True] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-5-False-True] 
dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-5-False-False] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_reduce_with_many_common_dependents[7-5-False-False] 
dask/tests/test_order.py::test_doublediff[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_doublediff[abcde] 
dask/tests/test_order.py::test_doublediff[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_doublediff[edcba] 
dask/tests/test_order.py::test_recursion_depth_long_linear_chains 
[gw0] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_unpicklable_args_generate_errors 
dask/tests/test_multiprocessing.py::test_reuse_pool[Pool] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_recursion_depth_long_linear_chains 
dask/tests/test_order.py::test_gh_3055_explicit[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_gh_3055_explicit[abcde] 
dask/tests/test_order.py::test_gh_3055_explicit[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_gh_3055_explicit[edcba] 
dask/tests/test_order.py::test_order_flox_reduction_2[abcde] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_order_flox_reduction_2[abcde] 
dask/tests/test_order.py::test_order_flox_reduction_2[edcba] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_order_flox_reduction_2[edcba] 
dask/tests/test_order.py::test_xarray_map_reduce_with_slicing 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_xarray_map_reduce_with_slicing 
dask/tests/test_order.py::test_xarray_rechunk_map_reduce_cohorts[True] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_xarray_rechunk_map_reduce_cohorts[True] 
dask/tests/test_order.py::test_xarray_rechunk_map_reduce_cohorts[False] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_xarray_rechunk_map_reduce_cohorts[False] 
dask/tests/test_order.py::test_xarray_8414 
[gw1] [ 97%] SKIPPED dask/tests/test_order.py::test_xarray_8414 
dask/tests/test_order.py::test_connecting_to_roots_single_root 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_connecting_to_roots_single_root 
dask/tests/test_order.py::test_connecting_to_roots_tree_reduction 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_connecting_to_roots_tree_reduction 
dask/tests/test_order.py::test_connecting_to_roots_long_linear_chains 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_connecting_to_roots_long_linear_chains 
dask/tests/test_order.py::test_connected_roots_deduplication_mem_usage 
[gw0] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_reuse_pool[Pool] 
dask/tests/test_multiprocessing.py::test_reuse_pool[ProcessPoolExecutor] 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_connected_roots_deduplication_mem_usage 
dask/tests/test_order.py::test_connecting_to_roots_asym 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_connecting_to_roots_asym 
dask/tests/test_order.py::test_do_not_mutate_input 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_do_not_mutate_input 
dask/tests/test_order.py::test_stackstac 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_stackstac 
dask/tests/test_order.py::test_handle_out_of_graph_dependencies 
[gw1] [ 97%] PASSED dask/tests/test_order.py::test_handle_out_of_graph_dependencies 
dask/tests/test_rewrite.py::test_head 
[gw1] [ 97%] PASSED dask/tests/test_rewrite.py::test_head 
dask/tests/test_rewrite.py::test_args 
[gw1] [ 97%] PASSED dask/tests/test_rewrite.py::test_args 
dask/tests/test_rewrite.py::test_traverser 
[gw1] [ 97%] PASSED dask/tests/test_rewrite.py::test_traverser 
dask/tests/test_rewrite.py::test_RewriteRule 
[gw1] [ 97%] PASSED dask/tests/test_rewrite.py::test_RewriteRule 
dask/tests/test_rewrite.py::test_RewriteRuleSubs 
[gw1] [ 97%] PASSED dask/tests/test_rewrite.py::test_RewriteRuleSubs 
dask/tests/test_rewrite.py::test_RuleSet 
[gw1] [ 97%] PASSED dask/tests/test_rewrite.py::test_RuleSet 
dask/tests/test_rewrite.py::test_matches 
[gw1] [ 97%] PASSED dask/tests/test_rewrite.py::test_matches 
dask/tests/test_rewrite.py::test_rewrite 
[gw1] [ 97%] PASSED dask/tests/test_rewrite.py::test_rewrite 
dask/tests/test_sizeof.py::test_base 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_base 
dask/tests/test_sizeof.py::test_name 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_name 
dask/tests/test_sizeof.py::test_containers 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_containers 
dask/tests/test_sizeof.py::test_bytes_like 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_bytes_like 
dask/tests/test_sizeof.py::test_numpy 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_numpy 
dask/tests/test_sizeof.py::test_numpy_0_strided 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_numpy_0_strided 
dask/tests/test_sizeof.py::test_pandas 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas 
dask/tests/test_sizeof.py::test_pandas_contiguous_dtypes 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_contiguous_dtypes 
dask/tests/test_sizeof.py::test_pandas_multiindex 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_multiindex 
dask/tests/test_sizeof.py::test_pandas_repeated_column 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_repeated_column 
dask/tests/test_sizeof.py::test_sparse_matrix 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_sparse_matrix 
dask/tests/test_sizeof.py::test_pandas_object_dtype[object-Series] 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_object_dtype[object-Series] 
dask/tests/test_sizeof.py::test_pandas_object_dtype[object-DataFrame] 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_object_dtype[object-DataFrame] 
dask/tests/test_sizeof.py::test_pandas_object_dtype[object-Index] 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_object_dtype[object-Index] 
dask/tests/test_sizeof.py::test_pandas_object_dtype[string[python]-Series] 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_object_dtype[string[python]-Series] 
dask/tests/test_sizeof.py::test_pandas_object_dtype[string[python]-DataFrame] 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_object_dtype[string[python]-DataFrame] 
dask/tests/test_sizeof.py::test_pandas_object_dtype[string[python]-Index] 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_object_dtype[string[python]-Index] 
dask/tests/test_sizeof.py::test_dataframe_object_dtype[object] 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_dataframe_object_dtype[object] 
dask/tests/test_sizeof.py::test_dataframe_object_dtype[string[python]] 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_dataframe_object_dtype[string[python]] 
dask/tests/test_sizeof.py::test_pandas_string_arrow_dtype[Series] 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_string_arrow_dtype[Series] 
dask/tests/test_sizeof.py::test_pandas_string_arrow_dtype[DataFrame] 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_string_arrow_dtype[DataFrame] 
dask/tests/test_sizeof.py::test_pandas_string_arrow_dtype[Index] 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_string_arrow_dtype[Index] 
dask/tests/test_sizeof.py::test_pandas_empty 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pandas_empty 
dask/tests/test_sizeof.py::test_pyarrow_table 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_pyarrow_table 
dask/tests/test_sizeof.py::test_dict 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_dict 
dask/tests/test_sizeof.py::test_register_backend_entrypoint 
[gw0] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_reuse_pool[ProcessPoolExecutor] 
dask/tests/test_multiprocessing.py::test_dumps_loads 
[gw0] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_dumps_loads 
dask/tests/test_multiprocessing.py::test_fuse_doesnt_clobber_intermediates 
[gw0] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_fuse_doesnt_clobber_intermediates 
dask/tests/test_multiprocessing.py::test_optimize_graph_false 
[gw0] [ 97%] PASSED dask/tests/test_multiprocessing.py::test_optimize_graph_false 
dask/tests/test_multiprocessing.py::test_works_with_highlevel_graph 
[gw1] [ 97%] PASSED dask/tests/test_sizeof.py::test_register_backend_entrypoint 
dask/tests/test_sizeof.py::test_xarray 
[gw1] [ 97%] SKIPPED dask/tests/test_sizeof.py::test_xarray 
dask/tests/test_sizeof.py::test_xarray_not_in_memory 
[gw1] [ 97%] SKIPPED dask/tests/test_sizeof.py::test_xarray_not_in_memory 
dask/tests/test_system.py::test_cpu_count 
[gw1] [ 97%] PASSED dask/tests/test_system.py::test_cpu_count 
dask/tests/test_system.py::test_cpu_count_cgroups[cpuacct,cpu] 
[gw1] [ 97%] PASSED dask/tests/test_system.py::test_cpu_count_cgroups[cpuacct,cpu] 
dask/tests/test_system.py::test_cpu_count_cgroups[cpu,cpuacct] 
[gw1] [ 97%] PASSED dask/tests/test_system.py::test_cpu_count_cgroups[cpu,cpuacct] 
dask/tests/test_system.py::test_cpu_count_cgroups[None] 
[gw1] [ 97%] PASSED dask/tests/test_system.py::test_cpu_count_cgroups[None] 
dask/tests/test_system.py::test_cpu_count_cgroups_v2[max-/] 
[gw1] [ 97%] PASSED dask/tests/test_system.py::test_cpu_count_cgroups_v2[max-/] 
dask/tests/test_system.py::test_cpu_count_cgroups_v2[max-/user.slice] 
[gw1] [ 97%] PASSED dask/tests/test_system.py::test_cpu_count_cgroups_v2[max-/user.slice] 
dask/tests/test_system.py::test_cpu_count_cgroups_v2[max-/user.slice/more.slice] 
[gw1] [ 97%] PASSED dask/tests/test_system.py::test_cpu_count_cgroups_v2[max-/user.slice/more.slice] 
dask/tests/test_system.py::test_cpu_count_cgroups_v2[2005-/] 
[gw1] [ 97%] PASSED dask/tests/test_system.py::test_cpu_count_cgroups_v2[2005-/] 
dask/tests/test_system.py::test_cpu_count_cgroups_v2[2005-/user.slice] 
[gw1] [ 97%] PASSED dask/tests/test_system.py::test_cpu_count_cgroups_v2[2005-/user.slice] 
dask/tests/test_system.py::test_cpu_count_cgroups_v2[2005-/user.slice/more.slice] 
[gw1] [ 97%] PASSED dask/tests/test_system.py::test_cpu_count_cgroups_v2[2005-/user.slice/more.slice] 
dask/tests/test_task_spec.py::test_convert_legacy_dsk_skip_new 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_convert_legacy_dsk_skip_new 
dask/tests/test_task_spec.py::test_repr 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_repr 
dask/tests/test_task_spec.py::test_task_eq 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_task_eq 
dask/tests/test_task_spec.py::test_convert_legacy_dsk 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_convert_legacy_dsk 
dask/tests/test_task_spec.py::test_task_executable 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_task_executable 
dask/tests/test_task_spec.py::test_task_nested_sequence 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_task_nested_sequence 
dask/tests/test_task_spec.py::test_reference_remote 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_reference_remote 
dask/tests/test_task_spec.py::test_reference_remote_twice_same 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_reference_remote_twice_same 
dask/tests/test_task_spec.py::test_reference_remote_twice_different 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_reference_remote_twice_different 
dask/tests/test_task_spec.py::test_task_nested 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_task_nested 
dask/tests/test_task_spec.py::test_pickle 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_pickle 
dask/tests/test_task_spec.py::test_pickle_size 
[gw1] [ 97%] PASSED dask/tests/test_task_spec.py::test_pickle_size 
dask/tests/test_task_spec.py::test_tokenize 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_tokenize 
dask/tests/test_task_spec.py::test_async_func 
[gw1] [ 98%] SKIPPED dask/tests/test_task_spec.py::test_async_func 
dask/tests/test_task_spec.py::test_parse_curry 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_curry 
dask/tests/test_task_spec.py::test_curry 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_curry 
dask/tests/test_task_spec.py::test_avoid_cycles 
[gw1] [ 98%] SKIPPED dask/tests/test_task_spec.py::test_avoid_cycles 
dask/tests/test_task_spec.py::test_runnable_as_kwarg 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_runnable_as_kwarg 
dask/tests/test_task_spec.py::test_dependency_as_kwarg 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_dependency_as_kwarg 
dask/tests/test_task_spec.py::test_array_as_argument 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_array_as_argument 
dask/tests/test_task_spec.py::test_ensure_slots[inst0] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_ensure_slots[inst0] 
dask/tests/test_task_spec.py::test_ensure_slots[inst1] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_ensure_slots[inst1] 
dask/tests/test_task_spec.py::test_ensure_slots[inst2] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_ensure_slots[inst2] 
dask/tests/test_task_spec.py::test_parse_graph_namedtuple_legacy[PlainNamedTuple-args0-kwargs0] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_graph_namedtuple_legacy[PlainNamedTuple-args0-kwargs0] 
dask/tests/test_task_spec.py::test_parse_graph_namedtuple_legacy[NewArgsNamedTuple-args1-kwargs1] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_graph_namedtuple_legacy[NewArgsNamedTuple-args1-kwargs1] 
dask/tests/test_task_spec.py::test_parse_graph_namedtuple_legacy[NewArgsExNamedTuple-args2-kwargs2] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_graph_namedtuple_legacy[NewArgsExNamedTuple-args2-kwargs2] 
dask/tests/test_task_spec.py::test_parse_namedtuple[PlainNamedTuple-args0-kwargs0] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_namedtuple[PlainNamedTuple-args0-kwargs0] 
dask/tests/test_task_spec.py::test_parse_namedtuple[NewArgsNamedTuple-args1-kwargs1] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_namedtuple[NewArgsNamedTuple-args1-kwargs1] 
dask/tests/test_task_spec.py::test_parse_namedtuple[NewArgsExNamedTuple-args2-kwargs2] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_namedtuple[NewArgsExNamedTuple-args2-kwargs2] 
dask/tests/test_task_spec.py::test_pickle_literals 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_pickle_literals 
dask/tests/test_task_spec.py::test_parse_non_task_inputs[set] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_non_task_inputs[set] 
dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj1] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj1] 
dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj2] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj2] 
dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj3] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj3] 
dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj4] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj4] 
dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj5] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj5] 
dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj6] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj6] 
dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj7] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_non_task_inputs[obj7] 
dask/tests/test_task_spec.py::test_resolve_aliases 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_resolve_aliases 
dask/tests/test_task_spec.py::test_resolve_multiple_aliases 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_resolve_multiple_aliases 
dask/tests/test_task_spec.py::test_convert_resolve 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_convert_resolve 
dask/tests/test_task_spec.py::test_parse_nested 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_parse_nested 
dask/tests/test_task_spec.py::test_sizeof 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_sizeof 
dask/tests/test_task_spec.py::test_execute_tasks_in_graph 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_execute_tasks_in_graph 
dask/tests/test_task_spec.py::test_deterministic_tokenization_respected 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_deterministic_tokenization_respected 
dask/tests/test_task_spec.py::test_keys_in_tasks 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_keys_in_tasks 
dask/tests/test_task_spec.py::test_dependencies_mapping_doesnt_mutate_task 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_dependencies_mapping_doesnt_mutate_task 
dask/tests/test_task_spec.py::test_fuse_tasks_key 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_fuse_tasks_key 
dask/tests/test_task_spec.py::test_fuse_tasks 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_fuse_tasks 
dask/tests/test_task_spec.py::test_fuse_reject_multiple_outputs 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_fuse_reject_multiple_outputs 
dask/tests/test_task_spec.py::test_fused_ensure_only_executed_once 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_fused_ensure_only_executed_once 
dask/tests/test_task_spec.py::test_fused_dont_hold_in_memory_too_long 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_fused_dont_hold_in_memory_too_long 
dask/tests/test_task_spec.py::test_linear_fusion 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_linear_fusion 
dask/tests/test_task_spec.py::test_linear_fusion_intermediate_branch 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_linear_fusion_intermediate_branch 
dask/tests/test_task_spec.py::test_linear_fusion_two_branches 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_linear_fusion_two_branches 
dask/tests/test_task_spec.py::test_linear_fusion_multiple_outputs 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_linear_fusion_multiple_outputs 
dask/tests/test_task_spec.py::test_nested_containers 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_nested_containers 
dask/tests/test_task_spec.py::test_block_io_fusion 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_block_io_fusion 
dask/tests/test_task_spec.py::test_data_producer 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_data_producer 
dask/tests/test_task_spec.py::test_nested_containers_different_types[Tuple-tuple] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_nested_containers_different_types[Tuple-tuple] 
dask/tests/test_task_spec.py::test_nested_containers_different_types[List-list] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_nested_containers_different_types[List-list] 
dask/tests/test_task_spec.py::test_nested_containers_different_types[Set-set] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_nested_containers_different_types[Set-set] 
dask/tests/test_task_spec.py::test_substitute 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_substitute 
dask/tests/test_task_spec.py::test_alias_task_ref_key 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_alias_task_ref_key 
dask/tests/test_task_spec.py::test_substitute_nested 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_substitute_nested 
dask/tests/test_task_spec.py::test_nested_containers_empty[Dict] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_nested_containers_empty[Dict] 
dask/tests/test_task_spec.py::test_nested_containers_empty[List] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_nested_containers_empty[List] 
dask/tests/test_task_spec.py::test_nested_containers_empty[Set] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_nested_containers_empty[Set] 
dask/tests/test_task_spec.py::test_nested_containers_empty[Tuple] 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_nested_containers_empty[Tuple] 
dask/tests/test_task_spec.py::test_substitute_subclasses 
[gw1] [ 98%] PASSED dask/tests/test_task_spec.py::test_substitute_subclasses 
dask/tests/test_threaded.py::test_get 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_get 
dask/tests/test_threaded.py::test_nested_get 
[gw0] [ 98%] PASSED dask/tests/test_multiprocessing.py::test_works_with_highlevel_graph 
dask/tests/test_multiprocessing.py::test_random_seeds[numpy] 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_nested_get 
dask/tests/test_threaded.py::test_get_without_computation 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_get_without_computation 
dask/tests/test_threaded.py::test_broken_callback 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_broken_callback 
dask/tests/test_threaded.py::test_exceptions_rise_to_top 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_exceptions_rise_to_top 
dask/tests/test_threaded.py::test_reuse_pool[ThreadPool] 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_reuse_pool[ThreadPool] 
dask/tests/test_threaded.py::test_reuse_pool[ThreadPoolExecutor] 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_reuse_pool[ThreadPoolExecutor] 
dask/tests/test_threaded.py::test_pool_kwarg[ThreadPool] 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_pool_kwarg[ThreadPool] 
dask/tests/test_threaded.py::test_pool_kwarg[ThreadPoolExecutor] 
[gw0] [ 98%] PASSED dask/tests/test_multiprocessing.py::test_random_seeds[numpy] 
dask/tests/test_multiprocessing.py::test_random_seeds[random] 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_pool_kwarg[ThreadPoolExecutor] 
dask/tests/test_threaded.py::test_threaded_within_thread 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_threaded_within_thread 
dask/tests/test_threaded.py::test_dont_spawn_too_many_threads 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_dont_spawn_too_many_threads 
dask/tests/test_threaded.py::test_dont_spawn_too_many_threads_CPU_COUNT 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_dont_spawn_too_many_threads_CPU_COUNT 
dask/tests/test_threaded.py::test_thread_safety 
[gw0] [ 98%] PASSED dask/tests/test_multiprocessing.py::test_random_seeds[random] 
dask/tests/test_multiprocessing.py::test_process_initializer[threading-None-expected_results0] 
[gw0] [ 98%] PASSED dask/tests/test_multiprocessing.py::test_process_initializer[threading-None-expected_results0] 
dask/tests/test_multiprocessing.py::test_process_initializer[processes-None-expected_results1] 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_thread_safety 
dask/tests/test_threaded.py::test_interrupt 
[gw1] [ 98%] PASSED dask/tests/test_threaded.py::test_interrupt 
dask/tests/test_tokenize.py::test_check_tokenize 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_check_tokenize 
dask/tests/test_tokenize.py::test_tokenize 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize 
dask/tests/test_tokenize.py::test_tokenize_scalar 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_scalar 
dask/tests/test_tokenize.py::test_tokenize_numpy_array_consistent_on_values 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_array_consistent_on_values 
dask/tests/test_tokenize.py::test_tokenize_numpy_array_supports_uneven_sizes 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_array_supports_uneven_sizes 
dask/tests/test_tokenize.py::test_tokenize_discontiguous_numpy_array 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_discontiguous_numpy_array 
dask/tests/test_tokenize.py::test_tokenize_numpy_datetime 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_datetime 
dask/tests/test_tokenize.py::test_tokenize_numpy_scalar 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_scalar 
dask/tests/test_tokenize.py::test_tokenize_numpy_scalar_string_rep 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_scalar_string_rep 
dask/tests/test_tokenize.py::test_tokenize_numpy_array_on_object_dtype 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_array_on_object_dtype 
dask/tests/test_tokenize.py::test_empty_numpy_array 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_empty_numpy_array 
dask/tests/test_tokenize.py::test_tokenize_numpy_memmap_offset 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_memmap_offset 
dask/tests/test_tokenize.py::test_tokenize_numpy_memmap 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_memmap 
dask/tests/test_tokenize.py::test_tokenize_numpy_memmap_no_filename 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_memmap_no_filename 
dask/tests/test_tokenize.py::test_tokenize_numpy_ufunc 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_ufunc 
dask/tests/test_tokenize.py::test_normalize_numpy_ufunc_unserializable 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_normalize_numpy_ufunc_unserializable 
dask/tests/test_tokenize.py::test_normalize_object_unserializable 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_normalize_object_unserializable 
dask/tests/test_tokenize.py::test_tokenize_partial_func_args_kwargs_consistent 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_partial_func_args_kwargs_consistent 
dask/tests/test_tokenize.py::test_normalize_base 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_normalize_base 
dask/tests/test_tokenize.py::test_tokenize_object 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_object 
dask/tests/test_tokenize.py::test_tokenize_local_functions[<lambda>0] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[<lambda>0] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[<lambda>1] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[<lambda>1] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[<lambda>2] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[<lambda>2] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[<lambda>3] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[<lambda>3] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[func0] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[func0] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[f2] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[f2] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[func1] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[func1] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[func2] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[func2] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[func3] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[func3] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[func4] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[func4] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[func5] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[func5] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[func6] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[func6] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[func7] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[func7] 
dask/tests/test_tokenize.py::test_tokenize_local_functions[func8] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions[func8] 
dask/tests/test_tokenize.py::test_tokenize_special_callables[getattr] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_special_callables[getattr] 
dask/tests/test_tokenize.py::test_tokenize_special_callables[join0] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_special_callables[join0] 
dask/tests/test_tokenize.py::test_tokenize_special_callables[join1] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_special_callables[join1] 
dask/tests/test_tokenize.py::test_tokenize_special_callables[__str__0] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_special_callables[__str__0] 
dask/tests/test_tokenize.py::test_tokenize_special_callables[__str__1] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_special_callables[__str__1] 
dask/tests/test_tokenize.py::test_tokenize_special_callables[f0] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_special_callables[f0] 
dask/tests/test_tokenize.py::test_tokenize_special_callables[f1] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_special_callables[f1] 
dask/tests/test_tokenize.py::test_tokenize_special_callables[g] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_special_callables[g] 
dask/tests/test_tokenize.py::test_tokenize_functions_unique_token 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_functions_unique_token 
dask/tests/test_tokenize.py::test_tokenize_local_classes_from_different_contexts[False] 
[gw1] [ 98%] XFAIL dask/tests/test_tokenize.py::test_tokenize_local_classes_from_different_contexts[False] 
dask/tests/test_tokenize.py::test_tokenize_local_classes_from_different_contexts[True] 
[gw1] [ 98%] XFAIL dask/tests/test_tokenize.py::test_tokenize_local_classes_from_different_contexts[True] 
dask/tests/test_tokenize.py::test_tokenize_local_functions_from_different_contexts 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_local_functions_from_different_contexts 
dask/tests/test_tokenize.py::test_tokenize_callable 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_callable 
dask/tests/test_tokenize.py::test_tokenize_composite_functions 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_composite_functions 
dask/tests/test_tokenize.py::test_tokenize_pandas 
[gw1] [ 98%] FAILED dask/tests/test_tokenize.py::test_tokenize_pandas 
dask/tests/test_tokenize.py::test_tokenize_pandas_invalid_unicode 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_pandas_invalid_unicode 
dask/tests/test_tokenize.py::test_tokenize_pandas_mixed_unicode_bytes 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_pandas_mixed_unicode_bytes 
dask/tests/test_tokenize.py::test_tokenize_pandas_cloudpickle 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_pandas_cloudpickle 
dask/tests/test_tokenize.py::test_tokenize_pandas_extension_array 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_pandas_extension_array 
dask/tests/test_tokenize.py::test_tokenize_na 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_na 
dask/tests/test_tokenize.py::test_tokenize_offset 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_offset 
dask/tests/test_tokenize.py::test_tokenize_pandas_index 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_pandas_index 
dask/tests/test_tokenize.py::test_tokenize_kwargs 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_kwargs 
dask/tests/test_tokenize.py::test_tokenize_same_repr 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_same_repr 
dask/tests/test_tokenize.py::test_tokenize_slotted 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_slotted 
dask/tests/test_tokenize.py::test_tokenize_slotted_no_value 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_slotted_no_value 
dask/tests/test_tokenize.py::test_tokenize_slots_and_dict 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_slots_and_dict 
dask/tests/test_tokenize.py::test_tokenize_method 
[gw0] [ 98%] PASSED dask/tests/test_multiprocessing.py::test_process_initializer[processes-None-expected_results1] 
dask/tests/test_multiprocessing.py::test_process_initializer[processes-proc_init-expected_results2] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_method 
dask/tests/test_tokenize.py::test_tokenize_callable_class 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_callable_class 
dask/tests/test_tokenize.py::test_tokenize_callable_class_with_tokenize_method 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_callable_class_with_tokenize_method 
dask/tests/test_tokenize.py::test_staticmethods 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_staticmethods 
dask/tests/test_tokenize.py::test_tokenize_sequences 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_sequences 
dask/tests/test_tokenize.py::test_nested_tokenize_seen 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_nested_tokenize_seen 
dask/tests/test_tokenize.py::test_tokenize_dict 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_dict 
dask/tests/test_tokenize.py::test_tokenize_set 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_set 
dask/tests/test_tokenize.py::test_tokenize_ordered_dict 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_ordered_dict 
dask/tests/test_tokenize.py::test_tokenize_dict_doesnt_call_str_on_values 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_dict_doesnt_call_str_on_values 
dask/tests/test_tokenize.py::test_tokenize_sorts_dict_before_seen_map 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_sorts_dict_before_seen_map 
dask/tests/test_tokenize.py::test_tokenize_sorts_set_before_seen_map 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_sorts_set_before_seen_map 
dask/tests/test_tokenize.py::test_tokenize_timedelta 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_timedelta 
dask/tests/test_tokenize.py::test_tokenize_enum[Enum] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_enum[Enum] 
dask/tests/test_tokenize.py::test_tokenize_enum[IntEnum] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_enum[IntEnum] 
dask/tests/test_tokenize.py::test_tokenize_enum[IntFlag] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_enum[IntFlag] 
dask/tests/test_tokenize.py::test_tokenize_enum[Flag] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_enum[Flag] 
dask/tests/test_tokenize.py::test_local_objects 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_local_objects 
dask/tests/test_tokenize.py::test_tokenize_dataclass 
[gw1] [ 98%] SKIPPED dask/tests/test_tokenize.py::test_tokenize_dataclass 
dask/tests/test_tokenize.py::test_tokenize_range[other0] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_range[other0] 
dask/tests/test_tokenize.py::test_tokenize_range[other1] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_range[other1] 
dask/tests/test_tokenize.py::test_tokenize_range[other2] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_range[other2] 
dask/tests/test_tokenize.py::test_tokenize_numpy_array 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_array 
dask/tests/test_tokenize.py::test_tokenize_object_array_with_nans 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_object_array_with_nans 
dask/tests/test_tokenize.py::test_tokenize_base_types[1] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[1] 
dask/tests/test_tokenize.py::test_tokenize_base_types[True] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[True] 
dask/tests/test_tokenize.py::test_tokenize_base_types[a0] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[a0] 
dask/tests/test_tokenize.py::test_tokenize_base_types[a1] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[a1] 
dask/tests/test_tokenize.py::test_tokenize_base_types[1.0] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[1.0] 
dask/tests/test_tokenize.py::test_tokenize_base_types[1j0] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[1j0] 
dask/tests/test_tokenize.py::test_tokenize_base_types[1j1] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[1j1] 
dask/tests/test_tokenize.py::test_tokenize_base_types[x7] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[x7] 
dask/tests/test_tokenize.py::test_tokenize_base_types[x8] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[x8] 
dask/tests/test_tokenize.py::test_tokenize_base_types[x9] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[x9] 
dask/tests/test_tokenize.py::test_tokenize_base_types[None] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[None] 
dask/tests/test_tokenize.py::test_tokenize_base_types[str] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[str] 
dask/tests/test_tokenize.py::test_tokenize_base_types[int] 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_base_types[int] 
dask/tests/test_tokenize.py::test_tokenize_literal 
[gw1] [ 98%] PASSED dask/tests/test_tokenize.py::test_tokenize_literal 
dask/tests/test_tokenize.py::test_tokenize_numpy_matrix 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_numpy_matrix 
dask/tests/test_tokenize.py::test_tokenize_dense_sparse_array[dok] 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_dense_sparse_array[dok] 
dask/tests/test_tokenize.py::test_tokenize_circular_recursion 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_circular_recursion 
dask/tests/test_tokenize.py::test_tokenize_datetime_date[other0] 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_datetime_date[other0] 
dask/tests/test_tokenize.py::test_tokenize_datetime_date[other1] 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_datetime_date[other1] 
dask/tests/test_tokenize.py::test_tokenize_datetime_date[other2] 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_datetime_date[other2] 
dask/tests/test_tokenize.py::test_tokenize_datetime_time 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_datetime_time 
dask/tests/test_tokenize.py::test_tokenize_datetime_datetime 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_datetime_datetime 
dask/tests/test_tokenize.py::test_tokenize_functions_main 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_functions_main 
dask/tests/test_tokenize.py::test_tokenize_dataclass_field_no_repr 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_dataclass_field_no_repr 
dask/tests/test_tokenize.py::test_tokenize_operator 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_operator 
dask/tests/test_tokenize.py::test_tokenize_random_state 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_random_state 
dask/tests/test_tokenize.py::test_tokenize_random_state_numpy 
[gw0] [ 99%] PASSED dask/tests/test_multiprocessing.py::test_process_initializer[processes-proc_init-expected_results2] 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_random_state_numpy 
dask/tests/test_multiprocessing.py::test_custom_context_used_python3_posix 
dask/tests/test_tokenize.py::test_tokenize_random_functions[random] 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_random_functions[random] 
dask/tests/test_tokenize.py::test_tokenize_random_functions[np.random] 
[gw0] [ 99%] PASSED dask/tests/test_multiprocessing.py::test_custom_context_used_python3_posix 
dask/tests/test_multiprocessing.py::test_get_context_using_python3_posix 
[gw0] [ 99%] PASSED dask/tests/test_multiprocessing.py::test_get_context_using_python3_posix 
dask/tests/test_multiprocessing.py::test_custom_context_ignored_elsewhere 
[gw0] [ 99%] SKIPPED dask/tests/test_multiprocessing.py::test_custom_context_ignored_elsewhere 
dask/tests/test_multiprocessing.py::test_get_context_always_default 
[gw0] [ 99%] SKIPPED dask/tests/test_multiprocessing.py::test_get_context_always_default 
dask/tests/test_optimization.py::test_cull 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_cull 
dask/tests/test_optimization.py::test_fuse 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_fuse 
dask/tests/test_optimization.py::test_fuse_keys 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_fuse_keys 
dask/tests/test_optimization.py::test_donot_substitute_same_key_multiple_times 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_donot_substitute_same_key_multiple_times 
dask/tests/test_optimization.py::test_inline 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_inline 
dask/tests/test_optimization.py::test_inline_functions 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_inline_functions 
dask/tests/test_optimization.py::test_inline_ignores_curries_and_partials 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_inline_ignores_curries_and_partials 
dask/tests/test_optimization.py::test_inline_functions_non_hashable 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_inline_functions_non_hashable 
dask/tests/test_optimization.py::test_inline_doesnt_shrink_fast_functions_at_top 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_inline_doesnt_shrink_fast_functions_at_top 
dask/tests/test_optimization.py::test_inline_traverses_lists 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_inline_traverses_lists 
dask/tests/test_optimization.py::test_inline_functions_protects_output_keys 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_inline_functions_protects_output_keys 
dask/tests/test_optimization.py::test_functions_of 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_functions_of 
dask/tests/test_optimization.py::test_inline_cull_dependencies 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_inline_cull_dependencies 
dask/tests/test_optimization.py::test_fuse_reductions_single_input 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_random_functions[np.random] 
dask/tests/test_tokenize.py::test_tokenize_random_functions_with_state 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_random_functions_with_state 
dask/tests/test_tokenize.py::test_tokenize_random_functions_with_state_numpy 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_fuse_reductions_single_input 
dask/tests/test_optimization.py::test_fuse_stressed 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_fuse_stressed 
dask/tests/test_optimization.py::test_fuse_reductions_multiple_input 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_fuse_reductions_multiple_input 
dask/tests/test_optimization.py::test_dont_fuse_numpy_arrays 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_dont_fuse_numpy_arrays 
dask/tests/test_optimization.py::test_fuse_config 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_fuse_config 
dask/tests/test_optimization.py::test_fused_keys_max_length 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_fused_keys_max_length 
dask/tests/test_optimization.py::test_hybrid_legacy_new 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_hybrid_legacy_new 
dask/tests/test_optimization.py::test_fusion_legacy_hybrid 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_fusion_legacy_hybrid 
dask/tests/test_optimization.py::test_fusion_wide_legacy_hybrid 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_fusion_wide_legacy_hybrid 
dask/tests/test_optimization.py::test_do_not_inline_legacy_hybrid 
[gw0] [ 99%] PASSED dask/tests/test_optimization.py::test_do_not_inline_legacy_hybrid 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_random_functions_with_state_numpy 
dask/tests/test_tokenize.py::test_tokenize_pyarrow_datatypes_simple 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_pyarrow_datatypes_simple 
dask/tests/test_order.py::test_visualize_int_overflow 
dask/tests/test_tokenize.py::test_tokenize_pyarrow_datatypes_complex 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_pyarrow_datatypes_complex 
dask/tests/test_tokenize.py::test_pyarrow_table 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_pyarrow_table 
dask/tests/test_tokenize.py::test_tokenize_opaque_object_with_buffers 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_opaque_object_with_buffers 
dask/tests/test_tokenize.py::test_tokenize_numba[numba_jit] 
[gw1] [ 99%] SKIPPED dask/tests/test_tokenize.py::test_tokenize_numba[numba_jit] 
dask/tests/test_tokenize.py::test_tokenize_numba[numba_jit_with_signature] 
[gw1] [ 99%] SKIPPED dask/tests/test_tokenize.py::test_tokenize_numba[numba_jit_with_signature] 
dask/tests/test_tokenize.py::test_tokenize_numba[numba_vectorize] 
[gw1] [ 99%] SKIPPED dask/tests/test_tokenize.py::test_tokenize_numba[numba_vectorize] 
dask/tests/test_tokenize.py::test_tokenize_numba[numba_vectorize_with_signature] 
[gw1] [ 99%] SKIPPED dask/tests/test_tokenize.py::test_tokenize_numba[numba_vectorize_with_signature] 
dask/tests/test_tokenize.py::test_tokenize_numba[numba_guvectorize] 
[gw1] [ 99%] SKIPPED dask/tests/test_tokenize.py::test_tokenize_numba[numba_guvectorize] 
dask/tests/test_tokenize.py::test_tokenize_numba_unique_token 
[gw1] [ 99%] SKIPPED dask/tests/test_tokenize.py::test_tokenize_numba_unique_token 
dask/tests/test_tokenize.py::test_numba_local 
[gw1] [ 99%] SKIPPED dask/tests/test_tokenize.py::test_numba_local 
dask/tests/test_tokenize.py::test_tokenize_np_dtype 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_np_dtype 
dask/tests/test_tokenize.py::test_tokenize_pandas_arrow_strings 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_pandas_arrow_strings 
dask/tests/test_tokenize.py::test_normalize_pickle 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_normalize_pickle 
dask/tests/test_tokenize.py::test_tokenize_recursive_respects_ensure_deterministic 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_recursive_respects_ensure_deterministic 
dask/tests/test_tokenize.py::test_tokenize_nested_sequence_thread_safe 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_visualize_int_overflow 
dask/tests/test_order.py::test_ordering_keeps_groups_together[abcde] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_ordering_keeps_groups_together[abcde] 
dask/tests/test_order.py::test_ordering_keeps_groups_together[edcba] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_ordering_keeps_groups_together[edcba] 
dask/tests/test_order.py::test_avoid_broker_nodes[abcde] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_avoid_broker_nodes[abcde] 
dask/tests/test_order.py::test_avoid_broker_nodes[edcba] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_avoid_broker_nodes[edcba] 
dask/tests/test_order.py::test_base_of_reduce_preferred[abcde-True] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_base_of_reduce_preferred[abcde-True] 
dask/tests/test_order.py::test_base_of_reduce_preferred[abcde-False] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_base_of_reduce_preferred[abcde-False] 
dask/tests/test_order.py::test_base_of_reduce_preferred[edcba-True] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_base_of_reduce_preferred[edcba-True] 
dask/tests/test_order.py::test_base_of_reduce_preferred[edcba-False] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_base_of_reduce_preferred[edcba-False] 
dask/tests/test_order.py::test_avoid_upwards_branching[abcde] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_avoid_upwards_branching[abcde] 
dask/tests/test_order.py::test_avoid_upwards_branching[edcba] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_avoid_upwards_branching[edcba] 
dask/tests/test_order.py::test_avoid_upwards_branching_complex[abcde] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_avoid_upwards_branching_complex[abcde] 
dask/tests/test_order.py::test_avoid_upwards_branching_complex[edcba] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_avoid_upwards_branching_complex[edcba] 
dask/tests/test_order.py::test_deep_bases_win_over_dependents[abcde] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_deep_bases_win_over_dependents[abcde] 
dask/tests/test_order.py::test_deep_bases_win_over_dependents[edcba] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_deep_bases_win_over_dependents[edcba] 
dask/tests/test_order.py::test_prefer_deep[abcde] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_prefer_deep[abcde] 
dask/tests/test_order.py::test_prefer_deep[edcba] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_prefer_deep[edcba] 
dask/tests/test_order.py::test_stacklimit[abcde] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_stacklimit[abcde] 
dask/tests/test_order.py::test_stacklimit[edcba] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_stacklimit[edcba] 
dask/tests/test_order.py::test_break_ties_by_str[abcde] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_break_ties_by_str[abcde] 
dask/tests/test_order.py::test_break_ties_by_str[edcba] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_break_ties_by_str[edcba] 
dask/tests/test_order.py::test_order_doesnt_fail_on_mixed_type_keys[abcde] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_order_doesnt_fail_on_mixed_type_keys[abcde] 
dask/tests/test_order.py::test_order_doesnt_fail_on_mixed_type_keys[edcba] 
[gw0] [ 99%] PASSED dask/tests/test_order.py::test_order_doesnt_fail_on_mixed_type_keys[edcba] 
dask/tests/test_traceback.py::test_distributed_shorten_traceback 
[gw0] [ 99%] SKIPPED dask/tests/test_traceback.py::test_distributed_shorten_traceback 
dask/tests/test_traceback.py::test_deprecated_config 
[gw0] [ 99%] PASSED dask/tests/test_traceback.py::test_deprecated_config 
dask/tests/test_typing.py::test_isinstance_core[DaskCollection] 
[gw0] [ 99%] PASSED dask/tests/test_typing.py::test_isinstance_core[DaskCollection] 
dask/tests/test_typing.py::test_isinstance_core[HLGDaskCollection] 
[gw0] [ 99%] PASSED dask/tests/test_typing.py::test_isinstance_core[HLGDaskCollection] 
dask/tests/test_typing.py::test_isinstance_custom 
[gw0] [ 99%] PASSED dask/tests/test_typing.py::test_isinstance_custom 
dask/tests/test_typing.py::test_parameter_passing 
[gw0] [ 99%] PASSED dask/tests/test_typing.py::test_parameter_passing 
dask/tests/test_typing.py::test_inheriting_class 
[gw0] [ 99%] PASSED dask/tests/test_typing.py::test_inheriting_class 
dask/tests/test_utils.py::test_ensure_bytes 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_ensure_bytes 
dask/tests/test_utils.py::test_ensure_bytes_ndarray 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_ensure_bytes_ndarray 
dask/tests/test_utils.py::test_ensure_bytes_pyarrow_buffer 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_ensure_bytes_pyarrow_buffer 
dask/tests/test_utils.py::test_ensure_unicode 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_ensure_unicode 
dask/tests/test_utils.py::test_ensure_unicode_ndarray 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_ensure_unicode_ndarray 
dask/tests/test_utils.py::test_ensure_unicode_pyarrow_buffer 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_ensure_unicode_pyarrow_buffer 
dask/tests/test_utils.py::test_getargspec 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_getargspec 
dask/tests/test_utils.py::test_takes_multiple_arguments 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_takes_multiple_arguments 
dask/tests/test_utils.py::test_dispatch 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_dispatch 
dask/tests/test_utils.py::test_dispatch_kwargs 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_dispatch_kwargs 
dask/tests/test_utils.py::test_dispatch_variadic_on_first_argument 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_dispatch_variadic_on_first_argument 
dask/tests/test_utils.py::test_dispatch_lazy 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_dispatch_lazy 
dask/tests/test_utils.py::test_dispatch_lazy_walks_mro 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_dispatch_lazy_walks_mro 
dask/tests/test_utils.py::test_random_state_data 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_random_state_data 
dask/tests/test_utils.py::test_memory_repr 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_memory_repr 
dask/tests/test_utils.py::test_method_caller 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_method_caller 
dask/tests/test_utils.py::test_skip_doctest 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_skip_doctest 
dask/tests/test_utils.py::test_extra_titles 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_extra_titles 
dask/tests/test_utils.py::test_asciitable 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_asciitable 
dask/tests/test_utils.py::test_SerializableLock 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_SerializableLock 
dask/tests/test_utils.py::test_SerializableLock_name_collision 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_SerializableLock_name_collision 
dask/tests/test_utils.py::test_SerializableLock_locked 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_SerializableLock_locked 
dask/tests/test_utils.py::test_SerializableLock_acquire_blocking 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_SerializableLock_acquire_blocking 
dask/tests/test_utils.py::test_funcname 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_funcname 
dask/tests/test_utils.py::test_funcname_long 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_funcname_long 
dask/tests/test_utils.py::test_funcname_toolz 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_funcname_toolz 
dask/tests/test_utils.py::test_funcname_multipledispatch 
[gw0] [ 99%] SKIPPED dask/tests/test_utils.py::test_funcname_multipledispatch 
dask/tests/test_utils.py::test_funcname_numpy_vectorize 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_funcname_numpy_vectorize 
dask/tests/test_utils.py::test_ndeepmap 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_ndeepmap 
dask/tests/test_utils.py::test_ensure_dict 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_ensure_dict 
dask/tests/test_utils.py::test_ensure_set 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_ensure_set 
dask/tests/test_utils.py::test_itemgetter 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_itemgetter 
dask/tests/test_utils.py::test_partial_by_order 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_partial_by_order 
dask/tests/test_utils.py::test_has_keyword 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_has_keyword 
dask/tests/test_utils.py::test_derived_from 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_derived_from 
dask/tests/test_utils.py::test_derived_from_prop_cached_prop[@property] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_derived_from_prop_cached_prop[@property] 
dask/tests/test_utils.py::test_derived_from_prop_cached_prop[@cached_property] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_derived_from_prop_cached_prop[@cached_property] 
dask/tests/test_utils.py::test_derived_from_func 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_derived_from_func 
dask/tests/test_utils.py::test_derived_from_dask_dataframe 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_derived_from_dask_dataframe 
dask/tests/test_utils.py::test_parse_bytes 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_parse_bytes 
dask/tests/test_utils.py::test_parse_timedelta 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_parse_timedelta 
dask/tests/test_utils.py::test_is_arraylike 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_is_arraylike 
dask/tests/test_utils.py::test_iter_chunks 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_iter_chunks 
dask/tests/test_utils.py::test_stringify 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_stringify 
dask/tests/test_utils.py::test_format_bytes[0-0 B] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[0-0 B] 
dask/tests/test_utils.py::test_format_bytes[920-920 B] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[920-920 B] 
dask/tests/test_utils.py::test_format_bytes[930-0.91 kiB] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[930-0.91 kiB] 
dask/tests/test_utils.py::test_format_bytes[943339.52-921.23 kiB] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[943339.52-921.23 kiB] 
dask/tests/test_utils.py::test_format_bytes[953579.52-0.91 MiB] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[953579.52-0.91 MiB] 
dask/tests/test_utils.py::test_format_bytes[965979668.48-921.23 MiB] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[965979668.48-921.23 MiB] 
dask/tests/test_utils.py::test_format_bytes[976465428.48-0.91 GiB] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[976465428.48-0.91 GiB] 
dask/tests/test_utils.py::test_format_bytes[989163180523.52-921.23 GiB] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[989163180523.52-921.23 GiB] 
dask/tests/test_utils.py::test_format_bytes[999900598763.52-0.91 TiB] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[999900598763.52-0.91 TiB] 
dask/tests/test_utils.py::test_format_bytes[1012903096856084.5-921.23 TiB] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[1012903096856084.5-921.23 TiB] 
dask/tests/test_utils.py::test_format_bytes[1023898213133844.5-0.91 PiB] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[1023898213133844.5-0.91 PiB] 
dask/tests/test_utils.py::test_format_bytes[1152921504606846976-1024.00 PiB] 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_bytes[1152921504606846976-1024.00 PiB] 
dask/tests/test_utils.py::test_format_time 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_format_time 
dask/tests/test_utils.py::test_deprecated 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_deprecated 
dask/tests/test_utils.py::test_deprecated_version 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_deprecated_version 
dask/tests/test_utils.py::test_deprecated_after_version 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_deprecated_after_version 
dask/tests/test_utils.py::test_deprecated_category 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_deprecated_category 
dask/tests/test_utils.py::test_deprecated_message 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_deprecated_message 
dask/tests/test_utils.py::test_typename 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_typename 
dask/tests/test_utils.py::test_typename_on_instances 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_typename_on_instances 
dask/tests/test_utils.py::test_cached_cumsum 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_cached_cumsum 
dask/tests/test_utils.py::test_cached_cumsum_nan 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_cached_cumsum_nan 
dask/tests/test_utils.py::test_cached_cumsum_non_tuple 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_cached_cumsum_non_tuple 
dask/tests/test_utils.py::test_tmpfile_naming 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_tmpfile_naming 
dask/tests/test_utils.py::test_get_meta_library 
[gw0] [ 99%] PASSED dask/tests/test_utils.py::test_get_meta_library 
dask/tests/test_utils.py::test_get_meta_library_gpu 
[gw0] [ 99%] SKIPPED dask/tests/test_utils.py::test_get_meta_library_gpu 
dask/tests/test_utils_test.py::test_hlg_layer 
[gw0] [ 99%] PASSED dask/tests/test_utils_test.py::test_hlg_layer 
dask/tests/test_utils_test.py::test_hlg_layer_topological 
[gw0] [ 99%] PASSED dask/tests/test_utils_test.py::test_hlg_layer_topological 
dask/tests/test_utils_test.py::test__check_warning 
[gw0] [ 99%] PASSED dask/tests/test_utils_test.py::test__check_warning 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_nested_sequence_thread_safe 
dask/tests/test_tokenize.py::test_tokenize_range_index 
[gw1] [ 99%] PASSED dask/tests/test_tokenize.py::test_tokenize_range_index 
dask/tests/test_traceback.py::test_shorten_traceback[None-expect0] 
[gw1] [ 99%] PASSED dask/tests/test_traceback.py::test_shorten_traceback[None-expect0] 
dask/tests/test_traceback.py::test_shorten_traceback[regexes1-expect1] 
[gw1] [ 99%] PASSED dask/tests/test_traceback.py::test_shorten_traceback[regexes1-expect1] 
dask/tests/test_traceback.py::test_shorten_traceback[regexes2-expect2] 
[gw1] [ 99%] PASSED dask/tests/test_traceback.py::test_shorten_traceback[regexes2-expect2] 
dask/tests/test_traceback.py::test_shorten_traceback[regexes3-expect3] 
[gw1] [ 99%] PASSED dask/tests/test_traceback.py::test_shorten_traceback[regexes3-expect3] 
dask/tests/test_traceback.py::test_shorten_traceback[regexes4-expect4] 
[gw1] [ 99%] PASSED dask/tests/test_traceback.py::test_shorten_traceback[regexes4-expect4] 
dask/tests/test_traceback.py::test_compute_shorten_traceback[threads] 
[gw1] [ 99%] PASSED dask/tests/test_traceback.py::test_compute_shorten_traceback[threads] 
dask/tests/test_traceback.py::test_compute_shorten_traceback[processes] 
[gw1] [ 99%] PASSED dask/tests/test_traceback.py::test_compute_shorten_traceback[processes] 
dask/tests/test_traceback.py::test_compute_shorten_traceback[sync] 
[gw1] [ 99%] PASSED dask/tests/test_traceback.py::test_compute_shorten_traceback[sync] 
dask/tests/test_traceback.py::test_persist_shorten_traceback[threads] 
[gw1] [ 99%] PASSED dask/tests/test_traceback.py::test_persist_shorten_traceback[threads] 
dask/tests/test_traceback.py::test_persist_shorten_traceback[processes] 
[gw1] [ 99%] PASSED dask/tests/test_traceback.py::test_persist_shorten_traceback[processes] 
dask/tests/test_traceback.py::test_persist_shorten_traceback[sync] 
[gw1] [100%] PASSED dask/tests/test_traceback.py::test_persist_shorten_traceback[sync] 

==================================== ERRORS ====================================
____ ERROR at setup of test_method_operators[series-True-rmul-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmul-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmul-1-0-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rmul-1-0-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rmul-1-0-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rmul-1-0-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmul-1-x-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rmul-1-x-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rmul-1-x-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rmul-1-x-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rdiv-None-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rdiv-None-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rdiv-None-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rdiv-None-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rdiv-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rdiv-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rdiv-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rdiv-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rdiv-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rdiv-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rdiv-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rdiv-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rdiv-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rdiv-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rdiv-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rdiv-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rdiv-1-0-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rdiv-1-0-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rdiv-1-0-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rdiv-1-0-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rdiv-1-x-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rdiv-1-x-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rdiv-1-x-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rdiv-1-x-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rtruediv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rtruediv-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rtruediv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rtruediv-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rtruediv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rtruediv-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rtruediv-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rtruediv-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rtruediv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rtruediv-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rtruediv-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rtruediv-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rtruediv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rtruediv-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rtruediv-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rtruediv-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rtruediv-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rtruediv-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rtruediv-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rtruediv-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rtruediv-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rtruediv-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rtruediv-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rtruediv-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rfloordiv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rfloordiv-None-None-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rfloordiv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rfloordiv-None-None-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rfloordiv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rfloordiv-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rfloordiv-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rfloordiv-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rfloordiv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rfloordiv-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rfloordiv-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rfloordiv-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rfloordiv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rfloordiv-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rfloordiv-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rfloordiv-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rfloordiv-1-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rfloordiv-1-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rfloordiv-1-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rfloordiv-1-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rfloordiv-1-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rfloordiv-1-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rfloordiv-1-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rfloordiv-1-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rmod-None-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmod-None-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rmod-None-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmod-None-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rmod-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmod-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmod-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmod-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rmod-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmod-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmod-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmod-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rmod-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmod-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmod-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmod-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmod-1-0-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rmod-1-0-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rmod-1-0-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rmod-1-0-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmod-1-x-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rmod-1-x-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rmod-1-x-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rmod-1-x-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rpow-None-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rpow-None-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rpow-None-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rpow-None-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rpow-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rpow-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rpow-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rpow-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rpow-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rpow-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rpow-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rpow-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rpow-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rpow-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rpow-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rpow-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rpow-1-0-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rpow-1-0-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rpow-1-0-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rpow-1-0-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rpow-1-x-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rpow-1-x-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rpow-1-x-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rpow-1-x-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-add-None-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-add-None-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-add-None-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-add-None-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-add-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-add-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-add-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-add-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-add-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-add-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-add-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-add-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-add-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-add-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-add-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-add-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-add-1-0-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-add-1-0-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-add-1-0-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-add-1-0-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-add-1-x-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-add-1-x-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-add-1-x-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-add-1-x-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-sub-None-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-sub-None-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-sub-None-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-sub-None-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-sub-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-sub-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-sub-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-sub-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-sub-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-sub-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-sub-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-sub-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-sub-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-sub-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-sub-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-sub-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-sub-1-0-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-sub-1-0-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-sub-1-0-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-sub-1-0-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-sub-1-x-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-sub-1-x-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-sub-1-x-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-sub-1-x-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-mul-None-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mul-None-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-mul-None-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mul-None-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-mul-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mul-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mul-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mul-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-mul-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mul-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mul-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mul-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-mul-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mul-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mul-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mul-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mul-1-0-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-mul-1-0-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-mul-1-0-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-mul-1-0-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mul-1-x-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-mul-1-x-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-mul-1-x-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-mul-1-x-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-div-None-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-div-None-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-div-None-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-div-None-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-div-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-div-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-div-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-div-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-div-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-div-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-div-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-div-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-div-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-div-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-div-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-div-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-div-1-0-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-div-1-0-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-div-1-0-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-div-1-0-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-div-1-x-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-div-1-x-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-div-1-x-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-div-1-x-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-divide-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-divide-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-divide-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-divide-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-divide-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-divide-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-divide-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-divide-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-divide-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-divide-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-divide-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-divide-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-divide-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-divide-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-divide-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-divide-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-divide-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-divide-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-divide-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-divide-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-divide-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-divide-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-divide-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-divide-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-truediv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-truediv-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-truediv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-truediv-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-truediv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-truediv-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-truediv-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-truediv-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-truediv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-truediv-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-truediv-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-truediv-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-truediv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-truediv-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-truediv-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-truediv-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-truediv-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-truediv-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-truediv-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-truediv-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-truediv-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-truediv-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-truediv-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-truediv-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-floordiv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-floordiv-None-None-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-floordiv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-floordiv-None-None-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-floordiv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-floordiv-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-floordiv-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-floordiv-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-floordiv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-floordiv-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-floordiv-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-floordiv-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-floordiv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-floordiv-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-floordiv-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-floordiv-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-floordiv-1-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-floordiv-1-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-floordiv-1-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-floordiv-1-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-floordiv-1-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-floordiv-1-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-floordiv-1-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-floordiv-1-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-mod-None-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mod-None-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-mod-None-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mod-None-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-mod-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mod-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mod-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mod-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-mod-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mod-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mod-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mod-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-mod-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mod-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mod-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-mod-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mod-1-0-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-mod-1-0-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-mod-1-0-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-mod-1-0-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-mod-1-x-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-mod-1-x-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-mod-1-x-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-mod-1-x-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-pow-None-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-pow-None-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-pow-None-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-pow-None-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-pow-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-pow-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-pow-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-pow-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-pow-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-pow-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-pow-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-pow-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-pow-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-pow-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-pow-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-False-pow-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-pow-1-0-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-pow-1-0-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-pow-1-0-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-pow-1-0-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-pow-1-x-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-pow-1-x-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-pow-1-x-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-pow-1-x-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-radd-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-radd-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-radd-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-radd-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-radd-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-radd-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-radd-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-radd-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-radd-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-radd-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-radd-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-radd-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-radd-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-radd-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-radd-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-radd-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-radd-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-radd-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-radd-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-radd-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-radd-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-radd-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-radd-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-radd-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rsub-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rsub-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rsub-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rsub-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rsub-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rsub-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rsub-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rsub-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rsub-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rsub-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rsub-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rsub-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rsub-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rsub-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rsub-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rsub-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rsub-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rsub-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rsub-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rsub-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rsub-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rsub-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rsub-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rsub-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rmul-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rmul-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rmul-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rmul-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rmul-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmul-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rmul-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmul-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rmul-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmul-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rmul-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmul-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rmul-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmul-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rmul-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmul-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rmul-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rmul-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmul-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rmul-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rmul-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rmul-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmul-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rmul-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rdiv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rdiv-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rdiv-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rdiv-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rdiv-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rdiv-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rdiv-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rdiv-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rdiv-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rdiv-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rdiv-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rdiv-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rdiv-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rdiv-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rdiv-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rdiv-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rdiv-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rdiv-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rdiv-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rdiv-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rdiv-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rdiv-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rdiv-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rdiv-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rtruediv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rtruediv-None-None-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rtruediv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rtruediv-None-None-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rtruediv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rtruediv-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rtruediv-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rtruediv-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rtruediv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rtruediv-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rtruediv-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rtruediv-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rtruediv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rtruediv-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rtruediv-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rtruediv-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rtruediv-1-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rtruediv-1-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rtruediv-1-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rtruediv-1-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rtruediv-1-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rtruediv-1-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rtruediv-1-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rtruediv-1-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-None-None-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-None-None-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rfloordiv-None-0-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-None-0-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rfloordiv-None-0-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rfloordiv-None-x-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-None-x-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rfloordiv-None-x-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rfloordiv-1-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-1-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rfloordiv-1-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-1-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rfloordiv-1-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rfloordiv-1-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rfloordiv-1-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rfloordiv-1-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rfloordiv-1-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rfloordiv-1-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rfloordiv-1-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rmod-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rmod-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rmod-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rmod-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rmod-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmod-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rmod-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmod-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rmod-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmod-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rmod-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmod-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rmod-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmod-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rmod-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmod-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rmod-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rmod-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmod-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rmod-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rmod-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rmod-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rmod-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rmod-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-False-rpow-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rpow-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rpow-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rpow-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rpow-None-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rpow-None-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rpow-None-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rpow-None-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rpow-None-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rpow-None-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rpow-None-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rpow-None-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-False-rpow-1-None-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rpow-1-None-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-False-rpow-1-None-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rpow-1-None-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rpow-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rpow-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rpow-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rpow-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-False-rpow-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rpow-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-False-rpow-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-False-rpow-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-add-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-add-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-add-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-add-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-add-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-add-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-add-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-add-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-add-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-add-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-add-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-add-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-add-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-add-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-add-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-add-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-add-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-add-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-add-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-add-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-add-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-add-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-add-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-add-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-sub-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-sub-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-sub-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-sub-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-sub-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-sub-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-sub-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-sub-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-sub-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-sub-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-sub-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-sub-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-sub-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-sub-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-sub-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-sub-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-sub-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-sub-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-sub-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-sub-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-sub-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-sub-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-sub-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-sub-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-mul-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mul-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-mul-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mul-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-mul-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mul-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mul-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mul-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-mul-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mul-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mul-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mul-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-mul-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mul-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mul-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mul-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mul-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-mul-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-mul-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-mul-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mul-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-mul-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-mul-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-mul-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-div-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-div-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-div-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-div-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-div-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-div-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-div-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-div-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-div-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-div-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-div-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-div-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-div-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-div-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-div-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-div-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-div-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-div-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-div-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-div-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-div-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-div-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-div-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-div-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-divide-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-divide-None-None-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-divide-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-divide-None-None-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-divide-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-divide-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-divide-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-divide-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-divide-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-divide-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-divide-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-divide-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-divide-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-divide-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-divide-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-divide-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-divide-1-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-divide-1-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-divide-1-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-divide-1-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-divide-1-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-divide-1-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-divide-1-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-divide-1-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-None-None-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-None-None-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-truediv-None-0-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-None-0-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-truediv-None-0-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-truediv-None-x-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-None-x-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-truediv-None-x-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-truediv-1-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-1-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-truediv-1-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-1-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-truediv-1-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-truediv-1-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-truediv-1-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-truediv-1-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-truediv-1-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-truediv-1-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-truediv-1-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-None-1] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-None-0] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-0-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-0-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-0-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-x-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-x-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-floordiv-None-x-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-floordiv-1-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-mod-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mod-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-mod-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mod-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-mod-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mod-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mod-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mod-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-mod-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mod-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mod-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mod-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-mod-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mod-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mod-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-mod-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mod-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-mod-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-mod-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-mod-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-mod-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-mod-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-mod-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-mod-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-pow-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-pow-None-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-pow-None-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-pow-None-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-pow-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-pow-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-pow-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-pow-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-pow-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-pow-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-pow-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-pow-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-pow-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-pow-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-pow-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-True-pow-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-pow-1-0-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-pow-1-0-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-pow-1-0-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-pow-1-0-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-pow-1-x-columns] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-pow-1-x-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-pow-1-x-index] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-pow-1-x-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-radd-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-radd-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-radd-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-radd-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-radd-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-radd-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-radd-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-radd-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-radd-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-radd-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-radd-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-radd-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-radd-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-radd-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-radd-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-radd-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-radd-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-radd-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-radd-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-radd-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-radd-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-radd-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-radd-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-radd-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rsub-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rsub-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rsub-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rsub-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rsub-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rsub-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rsub-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rsub-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rsub-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rsub-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rsub-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rsub-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rsub-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rsub-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rsub-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rsub-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rsub-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rsub-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rsub-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rsub-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rsub-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rsub-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rsub-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rsub-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rmul-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rmul-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rmul-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rmul-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rmul-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmul-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rmul-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmul-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rmul-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmul-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rmul-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmul-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rmul-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmul-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rmul-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmul-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rmul-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rmul-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmul-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rmul-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rmul-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rmul-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmul-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rmul-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rdiv-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rdiv-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-None-1] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-None-0] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-0-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-0-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-0-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-x-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-x-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rtruediv-None-x-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rtruediv-1-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-None-1] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-None-0] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-0-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-0-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-0-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-x-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-x-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-None-x-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-None-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-None-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rfloordiv-1-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rmod-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rmod-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rmod-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rmod-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rmod-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmod-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rmod-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmod-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rmod-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmod-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rmod-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmod-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rmod-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmod-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rmod-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmod-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rmod-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rmod-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmod-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rmod-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rmod-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rmod-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rmod-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rmod-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rpow-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rpow-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rpow-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rpow-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rpow-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rpow-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rpow-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rpow-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rpow-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rpow-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rpow-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rpow-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-True-rpow-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rpow-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-True-rpow-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rpow-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rpow-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rpow-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rpow-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rpow-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-True-rpow-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rpow-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-True-rpow-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-True-rpow-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-add-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-add-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-add-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-add-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-add-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-add-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-add-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-add-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-add-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-add-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-add-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-add-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-add-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-add-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-add-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-add-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-add-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-add-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-add-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-add-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-add-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-add-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-add-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-add-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-sub-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-sub-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-sub-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-sub-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-sub-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-sub-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-sub-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-sub-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-sub-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-sub-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-sub-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-sub-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-sub-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-sub-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-sub-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-sub-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-sub-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-sub-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-sub-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-sub-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-sub-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-sub-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-sub-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-sub-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-mul-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-mul-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-mul-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-mul-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-mul-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mul-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-mul-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mul-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-mul-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mul-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-mul-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mul-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-mul-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mul-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-mul-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mul-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-mul-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-mul-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mul-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-mul-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-mul-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-mul-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mul-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-mul-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-div-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-div-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-div-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-div-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-div-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-div-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-div-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-div-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-div-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-div-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-div-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-div-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-div-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-div-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-div-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-div-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-div-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-div-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-div-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-div-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-div-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-div-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-div-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-div-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-None-None-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-None-None-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-divide-None-0-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-None-0-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-divide-None-0-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-divide-None-x-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-None-x-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-divide-None-x-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-divide-1-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-1-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-divide-1-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-1-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-divide-1-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-divide-1-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-divide-1-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-divide-1-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-divide-1-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-divide-1-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-divide-1-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-None-None-1] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-None-None-0] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-truediv-None-0-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-None-0-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-truediv-None-0-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-truediv-None-x-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-None-x-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-truediv-None-x-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-truediv-1-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-1-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-truediv-1-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-1-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-truediv-1-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-truediv-1-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-truediv-1-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-truediv-1-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-truediv-1-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-truediv-1-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-truediv-1-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-None-1] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-None-0] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-0-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-0-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-0-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-x-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-x-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-floordiv-None-x-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-None-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-None-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-floordiv-1-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-mod-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-mod-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-mod-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-mod-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-mod-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mod-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-mod-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mod-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-mod-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mod-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-mod-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mod-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-mod-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mod-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-mod-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mod-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-mod-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-mod-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mod-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-mod-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-mod-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-mod-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-mod-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-mod-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-pow-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-pow-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-pow-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-pow-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-pow-None-0-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-pow-None-0-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-pow-None-0-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-pow-None-0-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-pow-None-x-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-pow-None-x-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-pow-None-x-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-pow-None-x-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-pow-1-None-columns] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-pow-1-None-1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-pow-1-None-index] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-pow-1-None-0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-pow-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-pow-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-pow-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-pow-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-pow-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-pow-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-pow-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[dataframe-False-pow-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-radd-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-radd-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-radd-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-radd-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-radd-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-radd-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-radd-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-radd-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-radd-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-radd-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-radd-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-radd-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-radd-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-radd-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-radd-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-radd-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-radd-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-radd-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-radd-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-radd-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-radd-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-radd-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-radd-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-radd-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rsub-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rsub-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rsub-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rsub-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rsub-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rsub-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rsub-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rsub-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rsub-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rsub-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_broadcasting_scalar[__add__] ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_align_partitions.py:20: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rsub-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_broadcasting_scalar[add] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_align_partitions.py:20: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rsub-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rsub-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_assign_unknown_partitions _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_align_partitions.py:20: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rsub-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rsub-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rsub-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rsub-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ ERROR at setup of test_del __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rsub-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_info[True-None-True] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rsub-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_info[True-None-False] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rsub-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_info[True-None-None] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rsub-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_info[True-StringIO-True] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rsub-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_info[True-StringIO-False] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rsub-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_info[True-StringIO-None] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rsub-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_info[False-None-True] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rmul-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_info[False-None-False] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmul-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_info[False-None-None] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rmul-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_info[False-StringIO-True] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmul-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_info[False-StringIO-False] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rmul-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_info[False-StringIO-None] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmul-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_info[None-None-True] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmul-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_info[None-None-False] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmul-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_info[None-None-None] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rmul-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_info[None-StringIO-True] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmul-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_info[None-StringIO-False] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmul-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_info[None-StringIO-None] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_dont_fuse_from_pandas_ops _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmul-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rmul-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_column_projection_modify_list _____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmul-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ ERROR at setup of test_setitem ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmul-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_set_index_blockwise_culling ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmul-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmul-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_explode_simplify ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rmul-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_dask __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rmul-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_cumulative_methods[cumsum] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_cumulative_methods[cumprod] ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rmul-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_cumulative_methods[cummin] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmul-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_cumulative_methods[cummax] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rmul-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_bool __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rmul-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_assign_empty_columns __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rmul-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ ERROR at setup of test_nbytes _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ ERROR at setup of test_pop __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_value_counts ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ ERROR at setup of test_dropna _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_reset_index_projections ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_shift[index-None-1] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_shift[index-None-2] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rdiv-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_shift[index-1h-1] ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_shift[index-1h-2] ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_shift[index-freq2-1] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_shift[index-freq2-2] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_shift[0-None-1] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_shift[0-None-2] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_shift[0-1h-1] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_shift[0-1h-2] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_shift[0-freq2-1] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_shift[0-freq2-2] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_shift[columns-None-1] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_shift[columns-None-2] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rdiv-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_shift[columns-1h-1] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_shift[columns-1h-2] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-None-1] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_shift[columns-freq2-1] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_shift[columns-freq2-2] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-None-0] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_shift[1-None-1] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_shift[1-None-2] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_shift[1-1h-1] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-0-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_shift[1-1h-2] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-0-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_shift[1-freq2-1] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-0-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_shift[1-freq2-2] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_memory_usage ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-x-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_nlargest_nsmallest[func0] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-x-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_nlargest_nsmallest[func1] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rtruediv-None-x-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_conditionals[<lambda>0] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_conditionals[<lambda>1] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-None-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_conditionals[<lambda>2] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_conditionals[<lambda>3] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_conditionals[<lambda>4] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-None-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_conditionals[<lambda>5] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_conditionals[<lambda>6] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_conditionals[<lambda>7] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_conditionals[<lambda>8] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_conditionals[<lambda>9] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_conditionals[<lambda>10] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_conditionals[<lambda>11] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_conditionals[<lambda>12] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_conditionals[<lambda>13] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rtruediv-1-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_conditionals[<lambda>14] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-None-1] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_conditionals[<lambda>15] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_conditionals[<lambda>16] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-None-0] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_conditionals[<lambda>17] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_conditionals[<lambda>18] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-0-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_conditionals[<lambda>19] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-0-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-0-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_conditionals[<lambda>20] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_diff[1-index] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-x-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_diff[1-0] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-x-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_diff[1-columns] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-None-x-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_diff[1-1] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_diff[1-None] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-None-1] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_diff[2-index] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_diff[2-0] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-None-0] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_diff[2-columns] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_diff[2-1] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-0-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_diff[2-None] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-0-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_diff[None-index] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-0-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_diff[None-0] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_diff[None-columns] ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-x-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_diff[None-1] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-x-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_diff[None-None] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rfloordiv-1-x-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-add-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rmod-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-add-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmod-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-add-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rmod-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmod-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-add-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rmod-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-add-None-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmod-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-add-None-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmod-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-add-None-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmod-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-add-None-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rmod-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-add-None-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmod-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-add-None-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmod-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmod-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-add-None-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rmod-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-add-None-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmod-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-add-1-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmod-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-add-1-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rmod-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-add-1-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmod-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-add-1-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rmod-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-add-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rmod-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-add-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rmod-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-add-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rmod-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-add-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rmod-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-add-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rmod-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-add-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rmod-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-add-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rpow-None-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rpow-None-None-1] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-add-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rpow-None-None-index] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-sub-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rpow-None-None-0] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-sub-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-sub-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rpow-None-0-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-sub-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rpow-None-0-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-sub-None-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rpow-None-0-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-sub-None-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rpow-None-0-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-sub-None-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rpow-None-x-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-sub-None-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rpow-None-x-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-sub-None-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rpow-None-x-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-sub-None-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rpow-None-x-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-sub-None-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[dataframe-False-rpow-1-None-columns] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-sub-None-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rpow-1-None-1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-sub-1-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rpow-1-None-index] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-sub-1-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[dataframe-False-rpow-1-None-0] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-sub-1-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rpow-1-0-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rpow-1-0-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-sub-1-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rpow-1-0-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rpow-1-0-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-sub-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[dataframe-False-rpow-1-x-columns] ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-sub-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rpow-1-x-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-sub-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[dataframe-False-rpow-1-x-index] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-sub-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[dataframe-False-rpow-1-x-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-sub-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-add-None-None-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-sub-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-add-None-None-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-sub-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-add-None-None-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-sub-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-add-None-None-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-mul-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-add-None-0-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-mul-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-add-None-0-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-mul-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-add-None-0-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-mul-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-add-None-0-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-mul-None-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-add-None-x-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-add-None-x-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mul-None-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-add-None-x-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-mul-None-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-add-None-x-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mul-None-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-add-1-None-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-mul-None-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-add-1-None-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mul-None-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-add-1-None-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-mul-None-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-add-1-None-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mul-None-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-add-1-0-columns] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-mul-1-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mul-1-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-add-1-0-1] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-mul-1-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-add-1-0-index] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mul-1-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-add-1-0-0] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-mul-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-add-1-x-columns] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-mul-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-add-1-x-1] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mul-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-add-1-x-index] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-mul-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-add-1-x-0] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-mul-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-sub-None-None-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-sub-None-None-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-mul-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-sub-None-None-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mul-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-sub-None-None-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-sub-None-0-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-mul-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-sub-None-0-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-div-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-sub-None-0-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-div-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-sub-None-0-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-div-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-sub-None-x-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-div-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-sub-None-x-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-div-None-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-sub-None-x-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-div-None-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-sub-None-x-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-div-None-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-sub-1-None-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-sub-1-None-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-div-None-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-sub-1-None-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-div-None-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-sub-1-None-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-div-None-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-sub-1-0-columns] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-div-None-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-sub-1-0-1] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-div-None-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-sub-1-0-index] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-div-1-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-sub-1-0-0] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-div-1-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-sub-1-x-columns] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-div-1-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-sub-1-x-1] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-sub-1-x-index] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-div-1-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-sub-1-x-0] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-div-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-mul-None-None-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-div-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mul-None-None-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-div-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-mul-None-None-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-div-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mul-None-None-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-div-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-mul-None-0-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-div-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mul-None-0-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-div-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-div-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mul-None-0-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-divide-None-None-columns] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mul-None-0-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-mul-None-x-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-divide-None-None-1] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mul-None-x-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-divide-None-None-index] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mul-None-x-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-divide-None-None-0] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mul-None-x-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-mul-1-None-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-divide-None-0-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mul-1-None-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-divide-None-0-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mul-1-None-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-divide-None-0-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mul-1-None-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-divide-None-0-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mul-1-0-columns] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-divide-None-x-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-mul-1-0-1] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-divide-None-x-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-mul-1-0-index] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-divide-None-x-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-mul-1-0-0] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-divide-None-x-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mul-1-x-columns] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-mul-1-x-1] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-divide-1-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-mul-1-x-index] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-divide-1-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-mul-1-x-0] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-divide-1-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-div-None-None-columns] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-divide-1-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-divide-1-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-div-None-None-1] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-divide-1-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-div-None-None-index] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-divide-1-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-div-None-None-0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-divide-1-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-div-None-0-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-divide-1-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-div-None-0-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-divide-1-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-div-None-0-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-divide-1-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-div-None-0-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-divide-1-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-div-None-x-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-truediv-None-None-columns] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-div-None-x-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-truediv-None-None-1] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-div-None-x-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-truediv-None-None-index] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-div-None-x-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-truediv-None-None-0] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-div-1-None-columns] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-truediv-None-0-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-div-1-None-1] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-truediv-None-0-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-div-1-None-index] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-truediv-None-0-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-div-1-None-0] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-truediv-None-0-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-div-1-0-columns] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-truediv-None-x-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-truediv-None-x-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-truediv-None-x-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-truediv-None-x-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-truediv-1-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-truediv-1-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-truediv-1-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-truediv-1-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-truediv-1-0-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-truediv-1-0-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-truediv-1-0-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-truediv-1-0-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-truediv-1-x-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-truediv-1-x-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-truediv-1-x-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-truediv-1-x-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-floordiv-None-None-columns] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-floordiv-None-None-1] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-floordiv-None-None-index] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-floordiv-None-None-0] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-floordiv-None-0-columns] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-floordiv-None-0-1] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-floordiv-None-0-index] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-floordiv-None-0-0] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-floordiv-None-x-columns] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-floordiv-None-x-1] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-floordiv-None-x-index] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-floordiv-None-x-0] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-floordiv-1-None-columns] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-floordiv-1-None-1] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-floordiv-1-None-index] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-floordiv-1-None-0] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-floordiv-1-0-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-floordiv-1-0-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-floordiv-1-0-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-floordiv-1-0-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-floordiv-1-x-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-floordiv-1-x-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-floordiv-1-x-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-floordiv-1-x-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-mod-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-mod-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-mod-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-mod-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-mod-None-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mod-None-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-mod-None-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mod-None-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-mod-None-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mod-None-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-mod-None-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mod-None-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-mod-1-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mod-1-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-mod-1-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mod-1-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-mod-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-mod-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mod-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-mod-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-mod-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-mod-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-mod-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-mod-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-pow-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-pow-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-pow-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-pow-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-pow-None-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-pow-None-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-pow-None-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-pow-None-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-pow-None-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-pow-None-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-pow-None-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-pow-None-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-pow-1-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-pow-1-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-pow-1-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-pow-1-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-pow-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-pow-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-pow-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-pow-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-pow-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-pow-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-pow-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[series-True-pow-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-radd-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-radd-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-radd-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-radd-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-radd-None-0-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-radd-None-0-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-radd-None-0-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-radd-None-0-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-radd-None-x-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-radd-None-x-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-radd-None-x-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-radd-None-x-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-radd-1-None-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-radd-1-None-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-radd-1-None-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-radd-1-None-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-radd-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-radd-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-radd-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-radd-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-radd-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-radd-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-radd-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-radd-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rsub-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rsub-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rsub-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rsub-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rsub-None-0-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rsub-None-0-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rsub-None-0-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rsub-None-0-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rsub-None-x-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rsub-None-x-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rsub-None-x-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rsub-None-x-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rsub-1-None-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rsub-1-None-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rsub-1-None-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rsub-1-None-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rsub-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rsub-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rsub-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rsub-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rsub-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rsub-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[series-True-rsub-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[series-True-rsub-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[series-True-rmul-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_iloc __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmul-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_iloc_errors ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[series-True-rmul-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_loc_slice _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_iloc_slice _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmul-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_columns_dtype_on_empty_slice[False-False] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rmul-None-0-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_columns_dtype_on_empty_slice[False-True] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_columns_dtype_on_empty_slice[True-False] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmul-None-0-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_columns_dtype_on_empty_slice[True-True] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmul-None-0-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ ERROR at setup of test_loc __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmul-None-0-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_loc_with_series ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_loc_with_array _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rmul-None-x-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_loc_with_function ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmul-None-x-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reverse_indexing ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(20)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_indexing.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10  10
11  11
12  12
13  13
14  14
15  15
16  16
17  17
18  18
19  19
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
5      50.0
6      60.0
7      70.0
8      80.0
9      90....12    120.0
13    130.0
14    140.0
15    150.0
16    160.0
17    170.0
18    180.0
19    190.0
Name: x, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[series-True-rmul-None-x-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_map_partitions _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmul-None-x-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_map_partitions_broadcast ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[series-True-rmul-1-None-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_map_overlap[1] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_map_overlap[4] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[series-True-rmul-1-None-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_map_overlap_divisions _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-div-1-0-1] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_map_partitions_partition_info _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-div-1-0-index] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_map_overlap_errors ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-div-1-0-0] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-div-1-x-columns] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-div-1-x-1] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-div-1-x-index] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-div-1-x-0] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-divide-None-None-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-divide-None-None-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-divide-None-None-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-divide-None-None-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-divide-None-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-divide-None-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-divide-None-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-divide-None-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-divide-None-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-divide-None-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-divide-None-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-divide-None-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-divide-1-None-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-divide-1-None-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-divide-1-None-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-divide-1-None-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-divide-1-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-divide-1-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-divide-1-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-divide-1-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-divide-1-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-divide-1-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-divide-1-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-divide-1-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-True-truediv-None-None-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-truediv-None-None-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-truediv-None-None-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-truediv-None-None-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-truediv-None-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-truediv-None-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-truediv-None-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-truediv-None-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-truediv-None-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-truediv-None-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-truediv-None-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-truediv-None-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-truediv-1-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-truediv-1-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-truediv-1-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-truediv-1-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-truediv-1-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-truediv-1-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-truediv-1-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-truediv-1-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-truediv-1-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-truediv-1-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-truediv-1-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-truediv-1-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-True-floordiv-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-floordiv-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-floordiv-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-floordiv-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-floordiv-None-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-floordiv-None-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-floordiv-None-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-floordiv-None-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-floordiv-None-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-floordiv-None-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-floordiv-None-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-floordiv-None-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-floordiv-1-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-floordiv-1-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-floordiv-1-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-floordiv-1-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-floordiv-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-floordiv-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-floordiv-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-floordiv-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-floordiv-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-floordiv-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-floordiv-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-floordiv-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-mod-None-None-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mod-None-None-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-mod-None-None-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mod-None-None-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-mod-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mod-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mod-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-True] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-False] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mod-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_token_given ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:18: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-mod-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mod-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mod-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mod-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-mod-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mod-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mod-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-mod-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mod-1-0-columns] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-mod-1-0-1] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-mod-1-0-index] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-mod-1-0-0] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-mod-1-x-columns] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-mod-1-x-1] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-mod-1-x-index] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-mod-1-x-0] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-pow-None-None-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-pow-None-None-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-pow-None-None-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-pow-None-None-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-pow-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-pow-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-pow-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-pow-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-pow-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-pow-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-pow-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-pow-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-pow-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-pow-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-pow-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-True-pow-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-pow-1-0-columns] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-pow-1-0-1] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-pow-1-0-index] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-pow-1-0-0] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-pow-1-x-columns] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-pow-1-x-1] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-pow-1-x-index] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-pow-1-x-0] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-radd-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-radd-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-radd-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-radd-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-radd-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-radd-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-radd-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-radd-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-radd-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-radd-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-radd-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-radd-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-radd-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-radd-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-radd-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-radd-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-radd-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-radd-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-radd-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-radd-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-radd-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-radd-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-radd-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-radd-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rsub-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rsub-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rsub-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rsub-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rsub-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rsub-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rsub-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rsub-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rsub-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rsub-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rsub-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rsub-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rsub-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rsub-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rsub-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rsub-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rsub-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rsub-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rsub-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rsub-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rsub-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rsub-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rsub-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rsub-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rmul-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rmul-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rmul-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rmul-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rmul-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmul-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rmul-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmul-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rmul-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmul-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rmul-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmul-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rmul-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmul-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rmul-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmul-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rmul-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rmul-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmul-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rmul-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rmul-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rmul-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmul-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rmul-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rdiv-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rdiv-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rdiv-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rdiv-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rdiv-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rdiv-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rdiv-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rdiv-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rdiv-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rdiv-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rdiv-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rdiv-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rdiv-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rdiv-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rdiv-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rdiv-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rdiv-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rdiv-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rdiv-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rdiv-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rdiv-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rdiv-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rdiv-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rdiv-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-True-rtruediv-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rtruediv-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-rtruediv-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rtruediv-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-rtruediv-None-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rtruediv-None-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rtruediv-None-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rtruediv-None-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-rtruediv-None-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rtruediv-None-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rtruediv-None-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rtruediv-None-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-rtruediv-1-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rtruediv-1-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rtruediv-1-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rtruediv-1-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rtruediv-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rtruediv-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rtruediv-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rtruediv-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rtruediv-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rtruediv-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rtruediv-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rtruediv-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[1-True-rfloordiv-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rfloordiv-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-True-rfloordiv-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rfloordiv-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-rfloordiv-None-0-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rfloordiv-None-0-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rfloordiv-None-0-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rfloordiv-None-0-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-rfloordiv-None-x-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rfloordiv-None-x-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rfloordiv-None-x-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rfloordiv-None-x-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-True-rfloordiv-1-None-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rfloordiv-1-None-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rfloordiv-1-None-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rfloordiv-1-None-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rfloordiv-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rfloordiv-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rfloordiv-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rfloordiv-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rfloordiv-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rfloordiv-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rfloordiv-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rfloordiv-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rmod-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rmod-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rmod-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rmod-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rmod-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmod-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rmod-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmod-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rmod-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmod-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rmod-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmod-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rmod-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_rewrite_filters ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100), "a": 1, "b": 2})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_predicate_pushdown.py:15: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x  a  b
0    0  1  2
1    1  1  2
2    2  1  2
3    3  1  2
4    4  1  2
..  .. .. ..
95  95  1  2
96  96  1  2
97  97  1  2
98  98  1  2
99  99  1  2

[100 rows x 3 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_rewrite_filters_query _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100), "a": 1, "b": 2})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_predicate_pushdown.py:15: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x  a  b
0    0  1  2
1    1  1  2
2    2  1  2
3    3  1  2
4    4  1  2
..  .. .. ..
95  95  1  2
96  96  1  2
97  97  1  2
98  98  1  2
99  99  1  2

[100 rows x 3 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmod-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rmod-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ ERROR at setup of test_median _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmod-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ ERROR at setup of test_min_dt _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rmod-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_drop_duplicates[1-False] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rmod-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_drop_duplicates[1-None] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_drop_duplicates[1-5] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmod-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_drop_duplicates[True-False] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rmod-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_drop_duplicates[True-None] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_drop_duplicates[True-5] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rmod-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_value_counts[1-False] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rmod-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_value_counts[1-None] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rmod-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_value_counts[1-5] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_value_counts[True-False] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rmod-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_value_counts[True-None] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_value_counts[True-5] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-True-rpow-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_value_counts_sort ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rpow-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_unique[1-None] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_unique[1-5] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rpow-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_unique[True-None] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_unique[True-5] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rpow-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_split_every[False-21-sum] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_dataframe_split_every[False-21-prod] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rpow-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_split_every[False-21-min] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rpow-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_split_every[False-21-max] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rpow-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_split_every[False-21-any] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_split_every[False-21-all] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rpow-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_dataframe_split_every[False-21-count] _________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rpow-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_split_every[None-23-sum] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_split_every[None-23-prod] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rpow-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_split_every[None-23-min] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rpow-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_split_every[None-23-max] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_split_every[None-23-any] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rpow-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_split_every[None-23-all] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_dataframe_split_every[None-23-count] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-True-rpow-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_dataframe_split_every[5-23-sum] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_dataframe_split_every[5-23-prod] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_dataframe_split_every[5-23-min] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rpow-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-True-rpow-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_dataframe_split_every[5-23-max] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_dataframe_split_every[5-23-any] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rpow-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_dataframe_split_every[5-23-all] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rpow-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_dataframe_split_every[5-23-count] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_dataframe_split_every[2-31-sum] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rpow-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_dataframe_split_every[2-31-prod] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rpow-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_dataframe_split_every[2-31-min] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_dataframe_split_every[2-31-max] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rpow-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_dataframe_split_every[2-31-any] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_dataframe_split_every[2-31-all] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-True-rpow-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_dataframe_split_every[2-31-count] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rpow-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_dataframe_mode_split_every[False-53] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_dataframe_mode_split_every[None-57] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-True-rpow-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_dataframe_mode_split_every[5-57] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-True-rpow-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_dataframe_mode_split_every[2-73] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_series_split_every[False-31-sum] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-add-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_series_split_every[False-31-prod] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-add-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_series_split_every[False-31-min] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_series_split_every[False-31-max] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-add-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_series_split_every[False-31-any] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_series_split_every[False-31-all] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-add-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_series_split_every[False-31-mode] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_series_split_every[False-31-count] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-add-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_series_split_every[None-33-sum] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_series_split_every[None-33-prod] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-add-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_series_split_every[None-33-min] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-add-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_series_split_every[None-33-max] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-add-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_series_split_every[None-33-any] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_series_split_every[None-33-all] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-add-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_series_split_every[None-33-mode] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-add-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_series_split_every[None-33-count] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-add-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[5-33-sum] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[5-33-prod] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-add-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[5-33-min] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-add-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[5-33-max] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[5-33-any] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-add-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[5-33-all] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-add-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-add-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[5-33-mode] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-add-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_series_split_every[5-33-count] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[2-41-sum] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-add-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[2-41-prod] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-add-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[2-41-min] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[2-41-max] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[2-41-any] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-add-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[2-41-all] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-add-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_series_split_every[2-41-mode] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-add-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_series_split_every[2-41-count] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-add-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[sum--1] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[sum-0] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-add-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[sum-1] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-sub-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_split_every_lt2[prod--1] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[prod-0] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-sub-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[prod-1] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-sub-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[min--1] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[min-0] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-sub-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[min-1] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-sub-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[max--1] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[max-0] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-sub-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[max-1] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-sub-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[any--1] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-sub-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[any-0] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[any-1] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-sub-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[all--1] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[all-0] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-sub-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[all-1] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-sub-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_split_every_lt2[mode--1] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[mode-0] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-sub-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_split_every_lt2[mode-1] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_split_every_lt2[count--1] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-sub-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_split_every_lt2[count-0] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-sub-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_split_every_lt2[count-1] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_split_every_lt2[nunique_approx--1] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-sub-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-sub-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_split_every_lt2[nunique_approx-0] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_split_every_lt2[nunique_approx-1] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-sub-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_split_every_lt2_split_out[drop_duplicates--1] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_split_every_lt2_split_out[drop_duplicates-0] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-sub-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_split_every_lt2_split_out[drop_duplicates-1] ______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-sub-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_split_every_lt2_split_out[unique--1] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_split_every_lt2_split_out[unique-0] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-sub-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_split_every_lt2_split_out[unique-1] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_split_every_lt2_split_out[value_counts--1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-sub-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_split_every_lt2_split_out[value_counts-0] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-sub-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_split_every_lt2_split_out[value_counts-1] _______
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_nunique_approx[None] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-sub-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_nunique_approx[False] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_nunique_approx[2] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-sub-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_nunique_approx[10] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_unique_base ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-mul-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_value_counts_split_out_normalize ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-mul-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reductions[func0] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reductions[func1] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-mul-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reductions[func2] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-mul-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reductions[func3] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reductions[func4] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-mul-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reductions[func5] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mul-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reductions[func6] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reductions[func7] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-mul-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reductions[func8] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reductions[func9] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mul-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_reductions[func10] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-mul-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_reductions[func11] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_reductions[func12] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mul-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_reductions[func13] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-mul-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mul-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_index_reductions[func0] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_index_reductions[func1] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-mul-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_index_reductions[func2] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mul-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_index_reductions[func3] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-mul-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_index_reductions[<lambda>] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mul-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_unimplemented_on_index[<lambda>] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-mul-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_unimplemented_on_index[func1] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_unimplemented_on_index[func2] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-mul-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_unimplemented_on_index[func3] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_unimplemented_on_index[func4] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mul-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_unimplemented_on_index[func5] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-mul-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_unimplemented_on_index[func6] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_unimplemented_on_index[func7] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-mul-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_cov_corr ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-mul-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mul-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-mul-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_mean_series_axis_none _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_reductions.py:19: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-div-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-div-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-div-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-div-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-div-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-div-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-div-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-div-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-div-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-div-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-div-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-div-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-div-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-div-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-div-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-div-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-div-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-div-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-div-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-div-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-div-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-div-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-div-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-div-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-False-divide-None-None-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-divide-None-None-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-divide-None-None-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-divide-None-None-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-divide-None-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-divide-None-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-divide-None-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-divide-None-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-divide-None-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-divide-None-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-divide-None-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-divide-None-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-divide-1-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-divide-1-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-divide-1-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-divide-1-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-divide-1-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-divide-1-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-divide-1-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-divide-1-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-divide-1-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-divide-1-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-divide-1-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-divide-1-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-False-truediv-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-truediv-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-truediv-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-truediv-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-truediv-None-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-truediv-None-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-truediv-None-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-truediv-None-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-truediv-None-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-truediv-None-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-truediv-None-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-truediv-None-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-truediv-1-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-truediv-1-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-truediv-1-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-truediv-1-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-truediv-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-truediv-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-truediv-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-truediv-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-truediv-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-truediv-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-truediv-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-truediv-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[1-False-floordiv-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-floordiv-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-False-floordiv-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-floordiv-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-floordiv-None-0-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-floordiv-None-0-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-floordiv-None-0-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-floordiv-None-0-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-floordiv-None-x-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-floordiv-None-x-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-floordiv-None-x-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-floordiv-None-x-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-floordiv-1-None-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-floordiv-1-None-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-floordiv-1-None-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-floordiv-1-None-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-floordiv-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-floordiv-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-floordiv-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-floordiv-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-floordiv-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-floordiv-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-floordiv-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-floordiv-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-mod-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-mod-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-mod-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-mod-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-mod-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mod-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-mod-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mod-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-mod-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mod-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-mod-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mod-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-mod-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mod-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-mod-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mod-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-mod-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-mod-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mod-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-mod-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-mod-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-mod-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-mod-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-mod-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-pow-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-pow-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-pow-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-pow-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-pow-None-0-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-pow-None-0-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-pow-None-0-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-pow-None-0-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-pow-None-x-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-pow-None-x-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-pow-None-x-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-pow-None-x-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-pow-1-None-columns] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-pow-1-None-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-pow-1-None-index] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-pow-1-None-0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-pow-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-pow-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-pow-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-pow-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-pow-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-pow-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-pow-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_method_operators[1-False-pow-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-radd-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-radd-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-radd-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-radd-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-radd-None-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-radd-None-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-radd-None-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-radd-None-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-radd-None-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-radd-None-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-radd-None-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-radd-None-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-radd-1-None-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-radd-1-None-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-radd-1-None-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-radd-1-None-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-radd-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-radd-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-radd-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-radd-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-radd-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-radd-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-radd-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-radd-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-rsub-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rsub-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rsub-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rsub-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rsub-None-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rsub-None-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rsub-None-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rsub-None-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rsub-None-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rsub-None-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rsub-None-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rsub-None-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rsub-1-None-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rsub-1-None-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rsub-1-None-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rsub-1-None-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rsub-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rsub-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rsub-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rsub-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rsub-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rsub-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rsub-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rsub-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-rmul-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmul-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rmul-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmul-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rmul-None-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmul-None-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmul-None-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmul-None-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rmul-None-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmul-None-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmul-None-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmul-None-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rmul-1-None-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmul-1-None-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmul-1-None-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmul-1-None-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmul-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rmul-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rmul-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rmul-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmul-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rmul-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rmul-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rmul-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-rdiv-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rdiv-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rdiv-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rdiv-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rdiv-None-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rdiv-None-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rdiv-None-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rdiv-None-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rdiv-None-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rdiv-None-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rdiv-None-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rdiv-None-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rdiv-1-None-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rdiv-1-None-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rdiv-1-None-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rdiv-1-None-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rdiv-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rdiv-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rdiv-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rdiv-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rdiv-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rdiv-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rdiv-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rdiv-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[1-False-rtruediv-None-None-columns] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rtruediv-None-None-1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-False-rtruediv-None-None-index] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rtruediv-None-None-0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-rtruediv-None-0-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_ufunc _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rtruediv-None-0-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_reducers[mean] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_reducers[std] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rtruediv-None-0-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_reducers[sum] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reducers[cumsum] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reducers[cumprod] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rtruediv-None-0-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_reducers[var] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_reducers[min] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-rtruediv-None-x-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_reducers[max] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_reducers[all] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rtruediv-None-x-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_reducers[any] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rtruediv-None-x-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_reducers[prod] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_clip __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_ufunc.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rtruediv-None-x-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-rtruediv-1-None-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rtruediv-1-None-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rtruediv-1-None-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rtruediv-1-None-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rtruediv-1-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rtruediv-1-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rtruediv-1-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rtruediv-1-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rtruediv-1-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rtruediv-1-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rtruediv-1-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rtruediv-1-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_method_operators[1-False-rfloordiv-None-None-columns] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rfloordiv-None-None-1] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-False-rfloordiv-None-None-index] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rfloordiv-None-None-0] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-False-rfloordiv-None-0-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rfloordiv-None-0-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-rfloordiv-None-0-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rfloordiv-None-0-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-False-rfloordiv-None-x-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rfloordiv-None-x-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-rfloordiv-None-x-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rfloordiv-None-x-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ ERROR at setup of test_method_operators[1-False-rfloordiv-1-None-columns] ___
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rfloordiv-1-None-1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-rfloordiv-1-None-index] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rfloordiv-1-None-0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rfloordiv-1-0-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rfloordiv-1-0-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rfloordiv-1-0-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rfloordiv-1-0-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rfloordiv-1-x-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rfloordiv-1-x-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rfloordiv-1-x-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rfloordiv-1-x-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-rmod-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmod-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rmod-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmod-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rmod-None-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmod-None-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmod-None-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmod-None-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rmod-None-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmod-None-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmod-None-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmod-None-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rmod-1-None-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmod-1-None-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmod-1-None-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rmod-1-None-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmod-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rmod-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rmod-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rmod-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rmod-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rmod-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rmod-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rmod-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___ ERROR at setup of test_method_operators[1-False-rpow-None-None-columns] ____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rpow-None-None-1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_method_operators[1-False-rpow-None-None-index] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rpow-None-None-0] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rpow-None-0-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rpow-None-0-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rpow-None-0-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rpow-None-0-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rpow-None-x-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rpow-None-x-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rpow-None-x-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rpow-None-x-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_method_operators[1-False-rpow-1-None-columns] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rpow-1-None-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rpow-1-None-index] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_method_operators[1-False-rpow-1-None-0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rpow-1-0-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rpow-1-0-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rpow-1-0-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rpow-1-0-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_method_operators[1-False-rpow-1-x-columns] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rpow-1-x-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_method_operators[1-False-rpow-1-x-index] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_method_operators[1-False-rpow-1-x-0] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_binary_operator[<lambda>0] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_binary_operator[<lambda>1] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_binary_operator[<lambda>2] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_binary_operator[<lambda>3] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_binary_operator[<lambda>4] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_and_or[<lambda>0] ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_and_or[<lambda>1] ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_to_timestamp[start] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_to_timestamp[end] ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>0] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>1] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>2] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>3] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>4] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>5] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>6] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>7] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>8] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>9] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>10] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>11] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>12] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>13] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>14] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>15] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>16] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>17] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>18] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>19] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>20] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>21] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>22] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>23] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>24] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>25] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>26] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>27] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>28] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>29] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>30] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>31] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>32] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>33] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>34] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>35] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>36] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>37] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>38] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>39] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>40] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>41] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>42] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>43] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>44] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>45] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>46] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_blockwise[<lambda>47] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_select_dtypes_projection ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ ERROR at setup of test_rename _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_mask_where _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_isna __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_clear_divisions ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_to_numeric _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_to_timedelta ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_drop_not_implemented __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_blockwise_pandas_only[<lambda>0] ____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_blockwise_pandas_only[<lambda>1] ____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_blockwise_pandas_only_warning[<lambda>0] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_blockwise_pandas_only_warning[<lambda>1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_blockwise_pandas_only_warning[<lambda>2] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_blockwise_pandas_only_warning[<lambda>3] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_map_meta ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_simplify_add_suffix_add_prefix _____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_rename_axis ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_series_name ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_isin __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_isin_repr _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_round _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_repr __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_combine_first_simplify _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_rename_traverse_filter _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_columns_traverse_filters ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_clip_traverse_filters _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_drop_duplicates_subset_simplify[x-zz0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_drop_duplicates_subset_simplify[x-projection1] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____ ERROR at setup of test_drop_duplicates_subset_simplify[x-projection2] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_drop_duplicates_subset_simplify[x-zz1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_drop_duplicates_subset_simplify[subset1-zz0] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_drop_duplicates_subset_simplify[subset1-projection1] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_drop_duplicates_subset_simplify[subset1-projection2] __
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_drop_duplicates_subset_simplify[subset1-zz1] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_broadcast _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ ERROR at setup of test_persist ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_index _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_reset_index[True] ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_reset_index[False] ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_head __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_head_down _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_case_when _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_head_head _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_head_npartitions ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_head_tail_repartition _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_tail __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_tail_down _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_tail_tail _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_tail_repartition ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_projection_stacking __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_projection_stacking_coercion ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_projection_pushdown_dim_0 _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_remove_unnecessary_projections _____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_substitute_parameters _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_copy __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_partitions _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_get_partition _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_column_getattr _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_serialization _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_size_optimized _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_series_iter ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_dataframe_iterrows ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_dataframe_itertuples __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_array_assignment ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ ERROR at setup of test_setattr ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_rename_projection_no_renamer ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_reset_index_projection_drop ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_getitem_column_types[list] _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_getitem_column_types[array] ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_getitem_column_types[Series] ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_getitem_column_types[Index] ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_random_partitions ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_depth _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_partitions_nested ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_repartition_npartitions[7-True] ____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_repartition_npartitions[7-False] ____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_repartition_npartitions[12-True] ____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_repartition_npartitions[12-False] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_repartition_divisions[True] ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_repartition_divisions[False] ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_repartition_no_op ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_repartition_partition_size _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ ERROR at setup of test_len __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_astype_simplify ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_drop_duplicates[1] ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_drop_duplicates[True] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_drop_duplicates_split_out _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_walk __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_find_operations ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_dropna_simplify[x] ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_dropna_simplify[subset1] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ ERROR at setup of test_dir __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ ERROR at setup of test_simplify_up_blockwise[x-replace-args0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_simplify_up_blockwise[x-isin-args1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_simplify_up_blockwise[x-clip-args2] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_simplify_up_blockwise[x-isna-args3] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_simplify_up_blockwise[x-round-args4] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_simplify_up_blockwise[x-abs-args5] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_simplify_up_blockwise[x-fillna-args6] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_simplify_up_blockwise[indexer1-replace-args0] _____
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_simplify_up_blockwise[indexer1-isin-args1] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_simplify_up_blockwise[indexer1-clip-args2] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_simplify_up_blockwise[indexer1-isna-args3] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______ ERROR at setup of test_simplify_up_blockwise[indexer1-round-args4] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ ERROR at setup of test_simplify_up_blockwise[indexer1-abs-args5] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____ ERROR at setup of test_simplify_up_blockwise[indexer1-fillna-args6] ______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_isin_as_predicate ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ ERROR at setup of test_sample _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_align _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_nunique[False] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_nunique[True] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_nunique_approx _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_memory_usage_per_partition _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_assign_simplify ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_assign_simplify_new_column_not_needed _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_assign_simplify_series _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_assign_non_series_inputs ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_assign_squash_together _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_rename_series_reduction ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_are_co_aligned _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_assign_pandas_inputs __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_astype_categories ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_drop_simplify _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_can_co_align ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_mixed_array_op _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_len_shuffle_repartition ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_columns_setter _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_contains ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_filter_pushdown_unavailable ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_filter_pushdown ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_shape _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_size __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_drop_duplicates_groupby ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_expression_bool_raises _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_expr_is_scalar _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_quantile_frame _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_quantile ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_dtype _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_shift_with_freq_datetime[B-False] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_shift_with_freq_datetime[D-True] ____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_shift_with_freq_datetime[h-True] ____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_isin_head _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_head_npartitions_divisions _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_shift_with_freq_period_index[D-True] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_shift_with_freq_period_index[h-True] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ ERROR at setup of test_shift_with_freq_TimedeltaIndex[min] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_shift_with_freq_TimedeltaIndex[D] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ ERROR at setup of test_shift_with_freq_TimedeltaIndex[h] ___________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_iter __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_items _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ ERROR at setup of test_predicate_pushdown_ndim_change _____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_axes __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_scalar_repr ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_array_conversion ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_dt_accessor ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def df_ddf():
        import numpy as np
    
        df = pd.DataFrame(
            {
                "str_col": ["abc", "bcd", "cdef", "DEFG"],
                "int_col": [1, 2, 3, 4],
                "dt_col": np.array(
                    [int(1e9), int(1.1e9), int(1.2e9), None], dtype="M8[ns]"
                ),
            },
            index=["E", "f", "g", "h"],
        )
    
>       df["string_col"] = df["str_col"].astype("string")

dask/dataframe/tests/test_accessors.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   str_col  int_col                  dt_col
E     abc        1 1970-01-01 00:00:01.000
f     bcd        2 1970-01-01 00:00:01.100
g    cdef        3 1970-01-01 00:00:01.200
h    DEFG        4                     NaT
key = 'string_col'
value = E     abc
f     bcd
g    cdef
h    DEFG
Name: str_col, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_reset_index_filter_pushdown ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_dt_accessor_not_available _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def df_ddf():
        import numpy as np
    
        df = pd.DataFrame(
            {
                "str_col": ["abc", "bcd", "cdef", "DEFG"],
                "int_col": [1, 2, 3, 4],
                "dt_col": np.array(
                    [int(1e9), int(1.1e9), int(1.2e9), None], dtype="M8[ns]"
                ),
            },
            index=["E", "f", "g", "h"],
        )
    
>       df["string_col"] = df["str_col"].astype("string")

dask/dataframe/tests/test_accessors.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   str_col  int_col                  dt_col
E     abc        1 1970-01-01 00:00:01.000
f     bcd        2 1970-01-01 00:00:01.100
g    cdef        3 1970-01-01 00:00:01.200
h    DEFG        4                     NaT
key = 'string_col'
value = E     abc
f     bcd
g    cdef
h    DEFG
Name: str_col, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_str_accessor ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def df_ddf():
        import numpy as np
    
        df = pd.DataFrame(
            {
                "str_col": ["abc", "bcd", "cdef", "DEFG"],
                "int_col": [1, 2, 3, 4],
                "dt_col": np.array(
                    [int(1e9), int(1.1e9), int(1.2e9), None], dtype="M8[ns]"
                ),
            },
            index=["E", "f", "g", "h"],
        )
    
>       df["string_col"] = df["str_col"].astype("string")

dask/dataframe/tests/test_accessors.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   str_col  int_col                  dt_col
E     abc        1 1970-01-01 00:00:01.000
f     bcd        2 1970-01-01 00:00:01.100
g    cdef        3 1970-01-01 00:00:01.200
h    DEFG        4                     NaT
key = 'string_col'
value = E     abc
f     bcd
g    cdef
h    DEFG
Name: str_col, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_astype_filter_pushdown _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_str_accessor_not_available _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def df_ddf():
        import numpy as np
    
        df = pd.DataFrame(
            {
                "str_col": ["abc", "bcd", "cdef", "DEFG"],
                "int_col": [1, 2, 3, 4],
                "dt_col": np.array(
                    [int(1e9), int(1.1e9), int(1.2e9), None], dtype="M8[ns]"
                ),
            },
            index=["E", "f", "g", "h"],
        )
    
>       df["string_col"] = df["str_col"].astype("string")

dask/dataframe/tests/test_accessors.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   str_col  int_col                  dt_col
E     abc        1 1970-01-01 00:00:01.000
f     bcd        2 1970-01-01 00:00:01.100
g    cdef        3 1970-01-01 00:00:01.200
h    DEFG        4                     NaT
key = 'string_col'
value = E     abc
f     bcd
g    cdef
h    DEFG
Name: str_col, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ ERROR at setup of test_drop_set ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_str_accessor_getitem __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def df_ddf():
        import numpy as np
    
        df = pd.DataFrame(
            {
                "str_col": ["abc", "bcd", "cdef", "DEFG"],
                "int_col": [1, 2, 3, 4],
                "dt_col": np.array(
                    [int(1e9), int(1.1e9), int(1.2e9), None], dtype="M8[ns]"
                ),
            },
            index=["E", "f", "g", "h"],
        )
    
>       df["string_col"] = df["str_col"].astype("string")

dask/dataframe/tests/test_accessors.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   str_col  int_col                  dt_col
E     abc        1 1970-01-01 00:00:01.000
f     bcd        2 1970-01-01 00:00:01.100
g    cdef        3 1970-01-01 00:00:01.200
h    DEFG        4                     NaT
key = 'string_col'
value = E     abc
f     bcd
g    cdef
h    DEFG
Name: str_col, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_shape_integer _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_str_accessor_extractall ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def df_ddf():
        import numpy as np
    
        df = pd.DataFrame(
            {
                "str_col": ["abc", "bcd", "cdef", "DEFG"],
                "int_col": [1, 2, 3, 4],
                "dt_col": np.array(
                    [int(1e9), int(1.1e9), int(1.2e9), None], dtype="M8[ns]"
                ),
            },
            index=["E", "f", "g", "h"],
        )
    
>       df["string_col"] = df["str_col"].astype("string")

dask/dataframe/tests/test_accessors.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   str_col  int_col                  dt_col
E     abc        1 1970-01-01 00:00:01.000
f     bcd        2 1970-01-01 00:00:01.100
g    cdef        3 1970-01-01 00:00:01.200
h    DEFG        4                     NaT
key = 'string_col'
value = E     abc
f     bcd
g    cdef
h    DEFG
Name: str_col, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_str_accessor_removeprefix_removesuffix[removeprefix] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def df_ddf():
        import numpy as np
    
        df = pd.DataFrame(
            {
                "str_col": ["abc", "bcd", "cdef", "DEFG"],
                "int_col": [1, 2, 3, 4],
                "dt_col": np.array(
                    [int(1e9), int(1.1e9), int(1.2e9), None], dtype="M8[ns]"
                ),
            },
            index=["E", "f", "g", "h"],
        )
    
>       df["string_col"] = df["str_col"].astype("string")

dask/dataframe/tests/test_accessors.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   str_col  int_col                  dt_col
E     abc        1 1970-01-01 00:00:01.000
f     bcd        2 1970-01-01 00:00:01.100
g    cdef        3 1970-01-01 00:00:01.200
h    DEFG        4                     NaT
key = 'string_col'
value = E     abc
f     bcd
g    cdef
h    DEFG
Name: str_col, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ ERROR at setup of test_str_accessor_removeprefix_removesuffix[removesuffix] __
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def df_ddf():
        import numpy as np
    
        df = pd.DataFrame(
            {
                "str_col": ["abc", "bcd", "cdef", "DEFG"],
                "int_col": [1, 2, 3, 4],
                "dt_col": np.array(
                    [int(1e9), int(1.1e9), int(1.2e9), None], dtype="M8[ns]"
                ),
            },
            index=["E", "f", "g", "h"],
        )
    
>       df["string_col"] = df["str_col"].astype("string")

dask/dataframe/tests/test_accessors.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   str_col  int_col                  dt_col
E     abc        1 1970-01-01 00:00:01.000
f     bcd        2 1970-01-01 00:00:01.100
g    cdef        3 1970-01-01 00:00:01.200
h    DEFG        4                     NaT
key = 'string_col'
value = E     abc
f     bcd
g    cdef
h    DEFG
Name: str_col, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_str_accessor_cat ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def df_ddf():
        import numpy as np
    
        df = pd.DataFrame(
            {
                "str_col": ["abc", "bcd", "cdef", "DEFG"],
                "int_col": [1, 2, 3, 4],
                "dt_col": np.array(
                    [int(1e9), int(1.1e9), int(1.2e9), None], dtype="M8[ns]"
                ),
            },
            index=["E", "f", "g", "h"],
        )
    
>       df["string_col"] = df["str_col"].astype("string")

dask/dataframe/tests/test_accessors.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   str_col  int_col                  dt_col
E     abc        1 1970-01-01 00:00:01.000
f     bcd        2 1970-01-01 00:00:01.100
g    cdef        3 1970-01-01 00:00:01.200
h    DEFG        4                     NaT
key = 'string_col'
value = E     abc
f     bcd
g    cdef
h    DEFG
Name: str_col, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_assign_overwriting_column _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_dropna_merge ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x // 7  # Not unique; duplicates span different partitions

dask/dataframe/dask_expr/tests/test_collection.py:52: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0      0
1      0
2      0
3      0
4      0
      ..
95    13
96    13
97    13
98    14
99    14
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_string_nullable_types _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def df_ddf():
        import numpy as np
    
        df = pd.DataFrame(
            {
                "str_col": ["abc", "bcd", "cdef", "DEFG"],
                "int_col": [1, 2, 3, 4],
                "dt_col": np.array(
                    [int(1e9), int(1.1e9), int(1.2e9), None], dtype="M8[ns]"
                ),
            },
            index=["E", "f", "g", "h"],
        )
    
>       df["string_col"] = df["str_col"].astype("string")

dask/dataframe/tests/test_accessors.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   str_col  int_col                  dt_col
E     abc        1 1970-01-01 00:00:01.000
f     bcd        2 1970-01-01 00:00:01.100
g    cdef        3 1970-01-01 00:00:01.200
h    DEFG        4                     NaT
key = 'string_col'
value = E     abc
f     bcd
g    cdef
h    DEFG
Name: str_col, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_concat_str _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ ERROR at setup of test_concat _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ ERROR at setup of test_concat_pdf _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_concat_divisions ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_concat_one_object ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ ERROR at setup of test_concat_one_no_columns _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ ERROR at setup of test_concat_simplify ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_concat_simplify_projection_not_added __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ ERROR at setup of test_concat_axis_one_co_aligned _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_concat_axis_one_all_divisions_unknown _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ ERROR at setup of test_concat_axis_one_drop_dfs_not_selected _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_concat_index ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ ERROR at setup of test_concat_one_series ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ ERROR at setup of test_concat_series _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ ERROR at setup of test_concat_series_and_projection ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_concat.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ ERROR at setup of test_simple _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_fusion.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_with_non_fusable_on_top ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_fusion.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ ERROR at setup of test_optimize_fusion_repeat _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_fusion.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ ERROR at setup of test_optimize_fusion_broadcast _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_fusion.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ ERROR at setup of test_persist_with_fusion __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_fusion.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ ERROR at setup of test_name __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.fixture
    def pdf():
        pdf = pd.DataFrame({"x": range(100)})
>       pdf["y"] = pdf.x * 10.0

dask/dataframe/dask_expr/tests/test_fusion.py:16: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'y'
value = 0       0.0
1      10.0
2      20.0
3      30.0
4      40.0
      ...  
95    950.0
96    960.0
97    970.0
98    980.0
99    990.0
Name: x, Length: 100, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
=================================== FAILURES ===================================
___________________ test_multiple_repartition_partition_size ___________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_multiple_repartition_partition_size():
        b = db.from_sequence(range(1, 100), npartitions=1)
        total_mem = sum(b.map_partitions(total_mem_usage).compute())
    
        c = b.repartition(partition_size=(total_mem // 2))
        assert c.npartitions >= 2
        assert_eq(b, c)
    
>       d = c.repartition(partition_size=(total_mem // 5))

dask/bag/tests/test_bag.py:1305: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
dask/bag/core.py:1696: in repartition
    return repartition_size(self, partition_size)
dask/bag/core.py:2668: in repartition_size
    mem_usages = bag.map_partitions(total_mem_usage).compute()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

exc = TypeError(TypeError("cannot pickle 'itertools.chain' object"), '  File "/builddir/build/BUILD/python-dask-2025.4.1-bui...           ~~~~~~~~^^^^^^^^^^^\n  File "/usr/lib64/python3.14/copy.py", line 146, in deepcopy\n    rv = reductor(4)\n')
tb = '  File "/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/local.py", l...            ~~~~~~~~^^^^^^^^^^^\n  File "/usr/lib64/python3.14/copy.py", line 146, in deepcopy\n    rv = reductor(4)\n'

    def reraise(exc, tb=None):
        exc = remote_exception(exc, tb)
>       raise exc
E       dask.multiprocessing.TypeError: cannot pickle 'itertools.chain' object
E       
E       Traceback
E       ---------
E         File "/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/local.py", line 252, in execute_task
E           result = task(data)
E         File "/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/_task_spec.py", line 758, in __call__
E           return self.func(*new_argspec)
E                  ~~~~~~~~~^^^^^^^^^^^^^^
E         File "/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/_task_spec.py", line 204, in _execute_subgraph
E           res = execute_graph(final, keys=[outkey])
E         File "/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/_task_spec.py", line 1028, in execute_graph
E           cache[key] = node(cache)
E                        ~~~~^^^^^^^
E         File "/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/_task_spec.py", line 758, in __call__
E           return self.func(*new_argspec)
E                  ~~~~~~~~~^^^^^^^^^^^^^^
E         File "/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/bag/core.py", line 2657, in total_mem_usage
E           partition = reify(deepcopy(partition))
E                             ~~~~~~~~^^^^^^^^^^^
E         File "/usr/lib64/python3.14/copy.py", line 146, in deepcopy
E           rv = reductor(4)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/multiprocessing.py:112: TypeError
____________ test_combine_similar[parquet-read_parquet-ReadParquet] ____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw1/test_combine_similar_parquet_r0')
fmt = 'parquet', read_func = <function read_parquet at 0x7ffb94db4670>
read_cls = <class 'dask.dataframe.dask_expr.io.parquet.ReadParquet'>

    @pytest.mark.parametrize(
        "fmt,read_func,read_cls",
        [("parquet", read_parquet, ReadParquet), ("csv", read_csv, FromMap)],
    )
    def test_combine_similar(tmpdir, fmt, read_func, read_cls):
        pdf = pd.DataFrame(
            {"x": [0, 1, 2, 3] * 4, "y": range(16), "z": [None, 1, 2, 3] * 4}
        )
        fn = _make_file(tmpdir, format=fmt, df=pdf)
        df = read_func(fn)
        df = df.replace(1, 100)
>       df["xx"] = df.x != 0

dask/dataframe/dask_expr/io/tests/test_io.py:257: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = 'xx'
value = Series([], Name: x, dtype: bool)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_combine_similar[csv-read_csv-FromMap] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw1/test_combine_similar_csv_read_0')
fmt = 'csv', read_func = <function make_reader.<locals>.read at 0x7ffb95f4dc70>
read_cls = <class 'dask.dataframe.dask_expr.io.io.FromMap'>

    @pytest.mark.parametrize(
        "fmt,read_func,read_cls",
        [("parquet", read_parquet, ReadParquet), ("csv", read_csv, FromMap)],
    )
    def test_combine_similar(tmpdir, fmt, read_func, read_cls):
        pdf = pd.DataFrame(
            {"x": [0, 1, 2, 3] * 4, "y": range(16), "z": [None, 1, 2, 3] * 4}
        )
        fn = _make_file(tmpdir, format=fmt, df=pdf)
        df = read_func(fn)
        df = df.replace(1, 100)
>       df["xx"] = df.x != 0

dask/dataframe/dask_expr/io/tests/test_io.py:257: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [Unnamed: 0, x, y, z]
Index: [], key = 'xx'
value = Series([], Name: x, dtype: bool)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_combine_similar_no_projection_on_one_branch _______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw1/test_combine_similar_no_projec0')

    def test_combine_similar_no_projection_on_one_branch(tmpdir):
        pdf = pd.DataFrame(
            {"x": [0, 1, 2, 3] * 4, "y": range(16), "z": [None, 1, 2, 3] * 4}
        )
        fn = _make_file(tmpdir, format="parquet", df=pdf)
        df = read_parquet(fn)
>       df["xx"] = df.x != 0

dask/dataframe/dask_expr/io/tests/test_io.py:288: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = 'xx'
value = Series([], Name: x, dtype: bool)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_map_partitions_assign_fusedio ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw1/test_map_partitions_assign_fus0')

    def test_map_partitions_assign_fusedio(tmpdir):
        data = {"country": ["se", "dk", "no"], "value": [0.1, 0.2, 0.3]}
        pd.DataFrame(data).to_parquet(tmpdir, partition_cols=["country"])
        df = dd.read_parquet(tmpdir)
        y_pred = df.map_partitions(len)
>       df["y_pred"] = y_pred

dask/dataframe/dask_expr/io/tests/test_io.py:537: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [value, country]
Index: [], key = 'y_pred'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_to_parquet[True] _____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw1/test_to_parquet_True_0')
write_metadata_file = True

    @pytest.mark.skipif(WINDOWS, reason="directory empty on windows")
    @pytest.mark.parametrize("write_metadata_file", [True, False])
    def test_to_parquet(tmpdir, write_metadata_file):
        pdf = pd.DataFrame({"x": [1, 4, 3, 2, 0, 5]})
        df = from_pandas(pdf, npartitions=2)
    
        # Check basic parquet round trip
        df.to_parquet(tmpdir, write_metadata_file=write_metadata_file)
        df2 = read_parquet(tmpdir, calculate_divisions=True)
        assert_eq(df, df2)
    
        # Check overwrite behavior
>       df["new"] = df["x"] + 1

dask/dataframe/dask_expr/io/tests/test_parquet.py:111: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'new'
value = Series([], Name: x, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_to_parquet[False] ____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw1/test_to_parquet_False_0')
write_metadata_file = False

    @pytest.mark.skipif(WINDOWS, reason="directory empty on windows")
    @pytest.mark.parametrize("write_metadata_file", [True, False])
    def test_to_parquet(tmpdir, write_metadata_file):
        pdf = pd.DataFrame({"x": [1, 4, 3, 2, 0, 5]})
        df = from_pandas(pdf, npartitions=2)
    
        # Check basic parquet round trip
        df.to_parquet(tmpdir, write_metadata_file=write_metadata_file)
        df2 = read_parquet(tmpdir, calculate_divisions=True)
        assert_eq(df, df2)
    
        # Check overwrite behavior
>       df["new"] = df["x"] + 1

dask/dataframe/dask_expr/io/tests/test_parquet.py:111: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'new'
value = Series([], Name: x, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_timestamp_divisions ___________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw1/test_timestamp_divisions0')

    def test_timestamp_divisions(tmpdir):
        pdf = pd.DataFrame.from_dict(
            {
                "Date": ["11/26/2017", "11/26/2017"],
                "Time": ["17:00:00.067", "17:00:00.102"],
                "Volume": [403, 3],
            }
        )
>       pdf["Timestamp"] = pd.to_datetime(pdf.Date) + pd.to_timedelta(pdf.Time)

dask/dataframe/dask_expr/io/tests/test_parquet.py:587: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =          Date          Time  Volume
0  11/26/2017  17:00:00.067     403
1  11/26/2017  17:00:00.102       3
key = 'Timestamp'
value = 0   2017-11-26 17:00:00.067
1   2017-11-26 17:00:00.102
dtype: datetime64[ns]

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_read_parquet_index_projection ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw1/test_read_parquet_index_projec0')

    def test_read_parquet_index_projection(tmpdir):
        df = from_array(
            np.zeros((201 * 10, 8), dtype=np.int64),
            columns=["val1", "val2", "val3", "val4", "val5", "val6", "val7", "tsprv"],
        )
        df = df.repartition(npartitions=201)
        df.to_parquet(tmpdir + "/index", write_index=False)
    
        df = read_parquet(tmpdir + "/index", calculate_divisions=True)
>       result = df.assign(dts=df.index - df.tsprv)

dask/dataframe/dask_expr/io/tests/test_parquet.py:607: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [val1, val2, val3, val4, val5, val6, val7, tsprv]
Index: []
key = 'dts', value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_assign_align_partitions _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_assign_align_partitions():
        pdf = pd.DataFrame({"x": [0] * 20, "y": range(20)})
        df = from_pandas(pdf, npartitions=2)
        s = pd.Series(range(10, 30))
        ds = from_pandas(s, npartitions=df.npartitions)
>       result = df.assign(z=ds)[["y", "z"]]

dask/dataframe/dask_expr/tests/test_align_partitions.py:84: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2814: in assign
    result = new_collection(expr.AssignAlign(result, k, v.expr))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:3520: in _meta
    return self._expr_cls(*self.operands)._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = 'z'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________________ test_categorize ________________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                   x       y
npartitions=2               
0              int64  string
3                ...     ...
5                ...     ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =    x       y
0  1  bcbbbc
1  2  bcbbbc
2  3  bcbbbc
3  4  bcbbbc
4  1  bcbbbc
5  2  bcbbbc

    def test_categorize(df, pdf):
>       df = df.categorize()

dask/dataframe/dask_expr/tests/test_categorical.py:39: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3802: in categorize
    return new_collection(Categorize(self, categories, index))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_categorical.py:175: in _meta
    return _categorize_block(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/categorical.py:25: in _categorize_block
    df[col] = df[col].astype(cat_dtype)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: category
Categories (1, string): [bcbbbc])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_categorical_set_index __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_categorical_set_index():
        df = pd.DataFrame({"x": [1, 2, 3, 4], "y": ["a", "b", "b", "c"]})
>       df["y"] = pd.Categorical(df["y"], categories=["a", "b", "c"], ordered=True)

dask/dataframe/dask_expr/tests/test_categorical.py:57: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x  y
0  1  a
1  2  b
2  3  b
3  4  c, key = 'y'
value = ['a', 'b', 'b', 'c']
Categories (3, object): ['a' < 'b' < 'c']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_categorize_drops_category_columns ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_categorize_drops_category_columns():
        pdf = pd.DataFrame({"a": [1, 2, 1, 2, 3], "b": 1})
        df = from_pandas(pdf)
>       df = df.categorize(columns=["a"])

dask/dataframe/dask_expr/tests/test_categorical.py:69: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3802: in categorize
    return new_collection(Categorize(self, categories, index))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_categorical.py:175: in _meta
    return _categorize_block(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/categorical.py:25: in _categorize_block
    df[col] = df[col].astype(cat_dtype)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = 'a'
value = Series([], Name: a, dtype: category
Categories (3, int64): [1, 2, 3])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________________ test_dot ___________________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_dot():
        import dask.array as da
    
        s1 = pd.Series([1, 2, 3, 4])
        s2 = pd.Series([4, 5, 6, 6])
        df = pd.DataFrame({"one": s1, "two": s2})
    
        dask_s1 = from_pandas(s1, npartitions=1)
        dask_df = from_pandas(df, npartitions=1)
        dask_s2 = from_pandas(s2, npartitions=1)
    
        assert_eq(s1.dot(s2), dask_s1.dot(dask_s2))
>       assert_eq(s1.dot(df), dask_s1.dot(dask_df))

dask/dataframe/dask_expr/tests/test_collection.py:356: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_0'
value = Index([], dtype='object')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_numeric_column_names ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.mark.filterwarnings("ignore:DataFrameGroupBy.apply operated ")
    def test_numeric_column_names():
        df = pd.DataFrame({0: [0, 1, 0, 1], 1: [1, 2, 3, 4], 2: [0, 1, 0, 1]})
        ddf = from_pandas(df, npartitions=2)
        assert_eq(ddf.groupby(0).sum(), df.groupby(0).sum())
        assert_eq(ddf.groupby([0, 2]).sum(), df.groupby([0, 2]).sum())
        expected = df.groupby(0).apply(lambda x: x)
>       assert_eq(
            ddf.groupby(0).apply(lambda x: x, meta=expected),
            expected,
        )

dask/dataframe/dask_expr/tests/test_groupby.py:595: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   0      1      2
npartitions=2                     
               int64  ...=df, observed=False, func=<function test_numeric_column_names.<locals>.<lambda> at 0x7f5bd847d900>, args=(), kwargs={})
b =      0  1  2
0           
0 0  0  1  0
  2  0  3  0
1 1  1  2  1
  3  1  4  1
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   0      1      2
npartitions=2                     
               int64  ...=df, observed=False, func=<function test_numeric_column_names.<locals>.<lambda> at 0x7f5bd847d900>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_numeric_column_names.<locals>.<lambda> at 0x7f5bd847d900>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-63523125ab168572b2461359c3a10120', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________________ test_apply_divisions _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

pdf =      y  z
x        
0    0  1
1    1  1
2    2  1
3    3  1
4    4  1
..  .. ..
5   95  1
6   96  1
7   97  1
8   98  1
9   99  1

[100 rows x 2 columns]

    @pytest.mark.filterwarnings("ignore:DataFrameGroupBy.apply operated ")
    def test_apply_divisions(pdf):
        pdf = pdf.set_index("x")
        df = from_pandas(pdf, npartitions=10)
        with pytest.warns(UserWarning, match="inferred from partial data"):
            result = df.groupby(["x", "y"]).apply(lambda x: x)
        assert df.divisions == result.divisions
>       assert_eq(result, pdf.groupby(["x", "y"]).apply(lambda x: x))

dask/dataframe/dask_expr/tests/test_groupby.py:608: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                    y      z
npartitions=10              
0               int64  int64
1    ...False, func=<function test_apply_divisions.<locals>.<lambda> at 0x7f5bb8be0d50>, meta=<no_default>, args=(), kwargs={})
b =          y  z
x y  x       
0 0  0   0  1
  10 0  10  1
  20 0  20  1
  30 0  30  1
  40 0  40  1
...     .. ..
9 59 9  59  1
  69 9  69  1
  79 9  79  1
  89 9  89  1
  99 9  99  1

[100 rows x 2 columns]
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                    y      z
npartitions=10              
0               int64  int64
1    ...False, func=<function test_apply_divisions.<locals>.<lambda> at 0x7f5bb8be0d50>, meta=<no_default>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_apply_divisions.<locals>.<lambda> at 0x7f5bb8be0d50>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-59b224210889cbf25d8df0b597a94555', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
___________________________ test_groupby_apply_args ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
0              int64  ...  ...
75               ...    ...    ...
99               ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]

    def test_groupby_apply_args(df, pdf):
        with pytest.warns(UserWarning, match="inferred from partial data"):
>           assert_eq(
                df.groupby("x").apply(lambda x, y: x + y, 1),
                pdf.groupby("x").apply(lambda x, y: x + y, 1),
            )

dask/dataframe/dask_expr/tests/test_groupby.py:653: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ..., func=<function test_groupby_apply_args.<locals>.<lambda> at 0x7f5bb8992560>, meta=<no_default>, args=(1,), kwargs={})
b =        x    y  z
x               
0 0    1    1  2
  10   1   11  2
  20   1   21  2
  30   1   31  2
  40   1   41  2.....  ... ..
9 59  10   60  2
  69  10   70  2
  79  10   80  2
  89  10   90  2
  99  10  100  2

[100 rows x 3 columns]
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ..., func=<function test_groupby_apply_args.<locals>.<lambda> at 0x7f5bb8992560>, meta=<no_default>, args=(1,), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_groupby_apply_args.<locals>.<lambda> at 0x7f5bb8992560>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-0202cf3215de2d113200c7e33900119b', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
________________________ test_groupby_udf_user_warning _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
0              int64  ...  ...
75               ...    ...    ...
99               ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]

    @pytest.mark.filterwarnings("ignore:DataFrameGroupBy.apply operated ")
    def test_groupby_udf_user_warning(df, pdf):
        def func(df):
            return df + 1
    
        expected = pdf.groupby("x").apply(func)
        with pytest.warns(UserWarning, match="`meta` is not specified"):
>           assert_eq(expected, df.groupby("x").apply(func))

dask/dataframe/dask_expr/tests/test_groupby.py:803: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a =        x    y  z
x               
0 0    1    1  2
  10   1   11  2
  20   1   21  2
  30   1   31  2
  40   1   41  2.....  ... ..
9 59  10   60  2
  69  10   70  2
  79  10   80  2
  89  10   90  2
  99  10  100  2

[100 rows x 3 columns]
b = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ..., func=<function test_groupby_udf_user_warning.<locals>.func at 0x7f5bb91ea400>, meta=<no_default>, args=(), kwargs={})
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ..., func=<function test_groupby_udf_user_warning.<locals>.func at 0x7f5bb91ea400>, meta=<no_default>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_groupby_udf_user_warning.<locals>.func at 0x7f5bb91ea400>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-4fb3a0501295a1a29145f1e85c7a3a53', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
__________________________ test_groupby_median_series __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_groupby_median_series():
        pdf = pd.DataFrame({"a": [1, 2, 3], "b": 1})
        df = from_pandas(pdf, npartitions=2)
>       assert_eq(
            df.a.groupby(df.b).median(),
            pdf.a.groupby(pdf.b).median(),
        )

dask/dataframe/dask_expr/tests/test_groupby.py:845: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_0'
value = Series([], Name: b, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_cumulative[cumsum-a-a] __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = 'a'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_cumulative[cumsum-a-c] __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = 'c'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_cumulative[cumsum-a-d] __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = 'd'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_cumulative[cumsum-a-sel3] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = ['a', 'b']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_cumulative[cumsum-a-sel4] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = ['c', 'd']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_cumulative[cumsum-key1-a] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = 'a'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_cumulative[cumsum-key1-c] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = 'c'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_cumulative[cumsum-key1-d] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = 'd'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[cumsum-key1-sel3] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = ['a', 'b']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[cumsum-key1-sel4] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = ['c', 'd']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_cumulative[cumprod-a-a] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = 'a'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_cumulative[cumprod-a-c] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = 'c'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_cumulative[cumprod-a-d] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = 'd'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[cumprod-a-sel3] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = ['a', 'b']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[cumprod-a-sel4] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = ['c', 'd']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[cumprod-key1-a] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = 'a'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[cumprod-key1-c] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = 'c'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[cumprod-key1-d] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = 'd'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[cumprod-key1-sel3] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = ['a', 'b']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[cumprod-key1-sel4] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = ['c', 'd']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_cumulative[cumcount-a-a] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = 'a'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_cumulative[cumcount-a-c] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = 'c'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_cumulative[cumcount-a-d] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = 'd'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[cumcount-a-sel3] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = ['a', 'b']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[cumcount-a-sel4] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = ['c', 'd']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[cumcount-key1-a] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = 'a'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[cumcount-key1-c] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = 'c'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[cumcount-key1-d] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = 'd'

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[cumcount-key1-sel3] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = ['a', 'b']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[cumcount-key1-sel4] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = ['c', 'd']

    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/dask_expr/tests/test_groupby.py:898: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_series_groupby_cumfunc_with_named_index[cumsum-1] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, func = 'cumsum'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_groupby.py:939: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:1459: in _layer
    for i in range(1, self.frame.npartitions):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_series_groupby_cumfunc_with_named_index[cumsum-2] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 2, func = 'cumsum'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_groupby.py:939: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_series_groupby_cumfunc_with_named_index[cumprod-1] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, func = 'cumprod'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_groupby.py:939: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:1459: in _layer
    for i in range(1, self.frame.npartitions):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_series_groupby_cumfunc_with_named_index[cumprod-2] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 2, func = 'cumprod'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_groupby.py:939: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_series_groupby_cumfunc_with_named_index[cumcount-1] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, func = 'cumcount'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_groupby.py:939: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:1459: in _layer
    for i in range(1, self.frame.npartitions):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_series_groupby_cumfunc_with_named_index[cumcount-2] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 2, func = 'cumcount'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_groupby.py:939: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_dataframe_groupby_agg_custom_sum _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_dataframe_groupby_agg_custom_sum():
        pandas_spec = {"b": "sum"}
        dask_spec = {"b": Aggregation("sum", lambda s: s.sum(), lambda s0: s0.sum())}
        df = pd.DataFrame({"g": [0, 0, 1] * 3, "b": [1, 2, 3] * 3})
        ddf = from_pandas(df, npartitions=2)
        expected = df.groupby("g").aggregate(pandas_spec)
        result = ddf.groupby("g").aggregate(dask_spec)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_groupby.py:957: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   b
npartitions=1       
               int64
                 ...
Dask Nam...regation(frame=df, arg={'b': <dask.dataframe.dask_expr._groupby.Aggregation object at 0x7f5bb8895550>}, observed=False)
b =    b
g   
0  9
1  9, serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   b
npartitions=1       
               int64
                 ...
Dask Nam...regation(frame=df, arg={'b': <dask.dataframe.dask_expr._groupby.Aggregation object at 0x7f5bb8895550>}, observed=False)

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_dataframe_groupby_agg_custom_sum.<locals>.<lambda> at 0x7f5bb8915010>
E                           when serializing dict item 'chunk'
E                           when serializing dask.dataframe.dask_expr._groupby.Aggregation state
E                           when serializing dask.dataframe.dask_expr._groupby.Aggregation object
E                           when serializing dict item 'b'
E                           when serializing dict item 'arg'
E                           when serializing tuple item 3
E                           when serializing dict item ('decomposablegroupbyaggregation-tree-061831fd96ff23fe50aa8a797611f557', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
____________________ test_groupby_getitem_apply_group_keys _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_groupby_getitem_apply_group_keys():
        pdf = pd.DataFrame(
            {
                "A": [0, 1] * 4,
                "B": [1] * 8,
            }
        )
        df = from_pandas(pdf, npartitions=4)
        result = df.groupby("A", group_keys=False).B.apply(lambda x: x, meta=("B", int))
        expected = pdf.groupby("A", group_keys=False).B.apply(lambda x: x)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_groupby.py:1072: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask Series Structure:
npartitions=4
    int64
      ...
      ...
      ...
      ...
Dask Name: groupbyapply, 3 expr...t_groupby_getitem_apply_group_keys.<locals>.<lambda> at 0x7f5bb91ea820>, meta=('B', <class 'int'>), args=(), kwargs={})
b = 0    1
1    1
2    1
3    1
4    1
5    1
6    1
7    1
Name: B, dtype: int64
serialize_graph = True, args = (), kwargs = {}
obj = Dask Series Structure:
npartitions=4
    int64
      ...
      ...
      ...
      ...
Dask Name: groupbyapply, 3 expr...t_groupby_getitem_apply_group_keys.<locals>.<lambda> at 0x7f5bb91ea820>, meta=('B', <class 'int'>), args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_groupby_getitem_apply_group_keys.<locals>.<lambda> at 0x7f5bb91ea820>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-567080334b4e321c52d43a54e6c613eb', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_______________________ test_map_partitions_merge[True] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

opt = True

    @pytest.mark.parametrize("opt", [True, False])
    def test_map_partitions_merge(opt):
        # Make simple left & right dfs
        pdf1 = pd.DataFrame({"x": range(20), "y": range(20)})
        df1 = from_pandas(pdf1, 2)
        pdf2 = pd.DataFrame({"x": range(0, 20, 2), "z": range(10)})
        df2 = from_pandas(pdf2, 1)
    
        # Partition-wise merge with map_partitions
        df3 = df1.map_partitions(
            lambda l, r: l.merge(r, on="x"),
            df2,
            enforce_metadata=False,
            clear_divisions=True,
        )
    
        # Check result with/without fusion
        expect = pdf1.merge(pdf2, on="x")
        df3 = (df3.optimize() if opt else df3)[list(expect.columns)]
>       assert_eq(df3, expect, check_index=False)

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:68: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   x      y      z
npartitions=2                     
               int64  ...   ...
                 ...    ...    ...
Dask Name: getitem, 4 expressions
Expr=MapPartitions(lambda)[['x', 'y', 'z']]
b =     x   y  z
0   0   0  0
1   2   2  1
2   4   4  2
3   6   6  3
4   8   8  4
5  10  10  5
6  12  12  6
7  14  14  7
8  16  16  8
9  18  18  9
serialize_graph = True, args = (), kwargs = {'check_index': False}
obj = Dask DataFrame Structure:
                   x      y      z
npartitions=2                     
               int64  ...   ...
                 ...    ...    ...
Dask Name: getitem, 4 expressions
Expr=MapPartitions(lambda)[['x', 'y', 'z']]

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_map_partitions_merge.<locals>.<lambda> at 0x7f5bb8b8e140>
E                           when serializing tuple item 6
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('lambda-76b9eba43b39d92cedd3e445c7f4b118', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_______________________ test_map_partitions_merge[False] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

opt = False

    @pytest.mark.parametrize("opt", [True, False])
    def test_map_partitions_merge(opt):
        # Make simple left & right dfs
        pdf1 = pd.DataFrame({"x": range(20), "y": range(20)})
        df1 = from_pandas(pdf1, 2)
        pdf2 = pd.DataFrame({"x": range(0, 20, 2), "z": range(10)})
        df2 = from_pandas(pdf2, 1)
    
        # Partition-wise merge with map_partitions
        df3 = df1.map_partitions(
            lambda l, r: l.merge(r, on="x"),
            df2,
            enforce_metadata=False,
            clear_divisions=True,
        )
    
        # Check result with/without fusion
        expect = pdf1.merge(pdf2, on="x")
        df3 = (df3.optimize() if opt else df3)[list(expect.columns)]
>       assert_eq(df3, expect, check_index=False)

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:68: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   x      y      z
npartitions=2                     
               int64  ...   ...
                 ...    ...    ...
Dask Name: getitem, 4 expressions
Expr=MapPartitions(lambda)[['x', 'y', 'z']]
b =     x   y  z
0   0   0  0
1   2   2  1
2   4   4  2
3   6   6  3
4   8   8  4
5  10  10  5
6  12  12  6
7  14  14  7
8  16  16  8
9  18  18  9
serialize_graph = True, args = (), kwargs = {'check_index': False}
obj = Dask DataFrame Structure:
                   x      y      z
npartitions=2                     
               int64  ...   ...
                 ...    ...    ...
Dask Name: getitem, 4 expressions
Expr=MapPartitions(lambda)[['x', 'y', 'z']]

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_map_partitions_merge.<locals>.<lambda> at 0x7f5bb8b8e140>
E                           when serializing tuple item 6
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('lambda-76b9eba43b39d92cedd3e445c7f4b118', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
________________________ test_map_overlap_provide_meta _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_map_overlap_provide_meta():
        df = pd.DataFrame(
            {"x": [1, 2, 4, 7, 11], "y": [1.0, 2.0, 3.0, 4.0, 5.0]}
        ).rename_axis("myindex")
        ddf = from_pandas(df, npartitions=2)
    
        # Provide meta spec, but not full metadata
        res = ddf.map_overlap(
            lambda df: df.rolling(2).sum(), 2, 0, meta={"x": "i8", "y": "i8"}
        )
        sol = df.rolling(2).sum()
>       assert_eq(res, sol)

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:185: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   x      y
npartitions=2              
0              int64  int64
3                ...    ...
4                ...    ...
Dask Name: lambda, 2 expressions
Expr=MapPartitions(lambda)
b =             x    y
myindex           
0         NaN  NaN
1         3.0  3.0
2         6.0  5.0
3        11.0  7.0
4        18.0  9.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   x      y
npartitions=2              
0              int64  int64
3                ...    ...
4                ...    ...
Dask Name: lambda, 2 expressions
Expr=MapPartitions(lambda)

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_map_overlap_provide_meta.<locals>.<lambda> at 0x7f5bb8b8fcc0>
E                           when serializing tuple item 1
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_overlap_chunk-d178b1a7c08d02c379a0b70c00daf87f', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
________________ test_map_partitions_propagates_index_metadata _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_map_partitions_propagates_index_metadata():
        index = pd.Series(list("abcde"), name="myindex")
        df = pd.DataFrame(
            {"A": np.arange(5, dtype=np.int32), "B": np.arange(10, 15, dtype=np.int32)},
            index=index,
        )
        ddf = from_pandas(df, npartitions=2)
        res = ddf.map_partitions(
            lambda df: df.assign(C=df.A + df.B),
            meta=[("A", "i4"), ("B", "i4"), ("C", "i4")],
        )
        sol = df.assign(C=df.A + df.B)
>       assert_eq(res, sol)

dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py:334: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   A      B      C
npartitions=2                     
a              int32  ...       ...    ...    ...
e                ...    ...    ...
Dask Name: lambda, 2 expressions
Expr=MapPartitions(lambda)
b =          A   B   C
myindex           
a        0  10  10
b        1  11  12
c        2  12  14
d        3  13  16
e        4  14  18
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   A      B      C
npartitions=2                     
a              int32  ...       ...    ...    ...
e                ...    ...    ...
Dask Name: lambda, 2 expressions
Expr=MapPartitions(lambda)

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_map_partitions_propagates_index_metadata.<locals>.<lambda> at 0x7f5bb8b8fb60>
E                           when serializing dict item '_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('lambda-d4994b077ce392b73c2078cccd30e6fa', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
____________________________ test_join[tasks-right] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', shuffle_method = 'tasks'

    @pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
    @pytest.mark.parametrize("shuffle_method", ["tasks", "disk"])
    def test_join(how, shuffle_method):
        # Make simple left & right dfs
        pdf1 = pd.DataFrame({"x": range(20), "y": range(20)})
        df1 = from_pandas(pdf1, 4)
        pdf2 = pd.DataFrame({"z": range(10)}, index=pd.Index(range(10), name="a"))
        df2 = from_pandas(pdf2, 2)
    
        # Partition-wise merge with map_partitions
>       df3 = df1.join(df2, on="x", how=how, shuffle_method=shuffle_method)

dask/dataframe/dask_expr/tests/test_merge.py:139: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3004: in join
    return self.merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2951: in merge
    return merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5616: in merge
    result = new_collection(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_merge.py:202: in _meta
    return make_meta(left.merge(right, **kwargs))
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        x    y  z
NaN  NaN  NaN  1
0.0  1.0  1.0  1
1.0  1.0  1.0  1
key = 'x', value = NaN    0
0.0    1
1.0    1
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_join[tasks-outer] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'outer', shuffle_method = 'tasks'

    @pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
    @pytest.mark.parametrize("shuffle_method", ["tasks", "disk"])
    def test_join(how, shuffle_method):
        # Make simple left & right dfs
        pdf1 = pd.DataFrame({"x": range(20), "y": range(20)})
        df1 = from_pandas(pdf1, 4)
        pdf2 = pd.DataFrame({"z": range(10)}, index=pd.Index(range(10), name="a"))
        df2 = from_pandas(pdf2, 2)
    
        # Partition-wise merge with map_partitions
>       df3 = df1.join(df2, on="x", how=how, shuffle_method=shuffle_method)

dask/dataframe/dask_expr/tests/test_merge.py:139: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3004: in join
    return self.merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2951: in merge
    return merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5616: in merge
    result = new_collection(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_merge.py:202: in _meta
    return make_meta(left.merge(right, **kwargs))
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        x    y  z
NaN  NaN  NaN  1
0.0  1.0  1.0  1
1.0  1.0  1.0  1
key = 'x', value = NaN    0
0.0    1
1.0    1
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_join[disk-right] _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', shuffle_method = 'disk'

    @pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
    @pytest.mark.parametrize("shuffle_method", ["tasks", "disk"])
    def test_join(how, shuffle_method):
        # Make simple left & right dfs
        pdf1 = pd.DataFrame({"x": range(20), "y": range(20)})
        df1 = from_pandas(pdf1, 4)
        pdf2 = pd.DataFrame({"z": range(10)}, index=pd.Index(range(10), name="a"))
        df2 = from_pandas(pdf2, 2)
    
        # Partition-wise merge with map_partitions
>       df3 = df1.join(df2, on="x", how=how, shuffle_method=shuffle_method)

dask/dataframe/dask_expr/tests/test_merge.py:139: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3004: in join
    return self.merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2951: in merge
    return merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5616: in merge
    result = new_collection(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_merge.py:202: in _meta
    return make_meta(left.merge(right, **kwargs))
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        x    y  z
NaN  NaN  NaN  1
0.0  1.0  1.0  1
1.0  1.0  1.0  1
key = 'x', value = NaN    0
0.0    1
1.0    1
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_join[disk-outer] _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'outer', shuffle_method = 'disk'

    @pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
    @pytest.mark.parametrize("shuffle_method", ["tasks", "disk"])
    def test_join(how, shuffle_method):
        # Make simple left & right dfs
        pdf1 = pd.DataFrame({"x": range(20), "y": range(20)})
        df1 = from_pandas(pdf1, 4)
        pdf2 = pd.DataFrame({"z": range(10)}, index=pd.Index(range(10), name="a"))
        df2 = from_pandas(pdf2, 2)
    
        # Partition-wise merge with map_partitions
>       df3 = df1.join(df2, on="x", how=how, shuffle_method=shuffle_method)

dask/dataframe/dask_expr/tests/test_merge.py:139: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3004: in join
    return self.merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2951: in merge
    return merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5616: in merge
    result = new_collection(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_merge.py:202: in _meta
    return make_meta(left.merge(right, **kwargs))
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        x    y  z
NaN  NaN  NaN  1
0.0  1.0  1.0  1
1.0  1.0  1.0  1
key = 'x', value = NaN    0
0.0    1
1.0    1
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_singleton_divisions ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_singleton_divisions():
        df = pd.DataFrame({"x": [1, 1, 1]}, index=[1, 2, 3])
        ddf = from_pandas(df, npartitions=2)
        ddf2 = ddf.set_index("x")
    
        joined = ddf2.join(ddf2, rsuffix="r")
        assert joined.divisions == (1, 1)
>       joined.compute()

dask/dataframe/dask_expr/tests/test_merge.py:190: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_merge_combine_similar[2-3] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions_left = 2, npartitions_right = 3

    @pytest.mark.parametrize("npartitions_left, npartitions_right", [(2, 3), (1, 1)])
    def test_merge_combine_similar(npartitions_left, npartitions_right):
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                "b": 1,
                "c": 1,
                "d": 1,
                "e": 1,
                "f": 1,
            }
        )
        pdf2 = pd.DataFrame({"a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "x": 1})
    
        df = from_pandas(pdf, npartitions=npartitions_left)
        df2 = from_pandas(pdf2, npartitions=npartitions_right)
    
        query = df.merge(df2)
>       query["new"] = query.b + query.c

dask/dataframe/dask_expr/tests/test_merge.py:243: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b, c, d, e, f, x]
Index: [], key = 'new'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_merge_combine_similar[1-1] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions_left = 1, npartitions_right = 1

    @pytest.mark.parametrize("npartitions_left, npartitions_right", [(2, 3), (1, 1)])
    def test_merge_combine_similar(npartitions_left, npartitions_right):
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                "b": 1,
                "c": 1,
                "d": 1,
                "e": 1,
                "f": 1,
            }
        )
        pdf2 = pd.DataFrame({"a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "x": 1})
    
        df = from_pandas(pdf, npartitions=npartitions_left)
        df2 = from_pandas(pdf2, npartitions=npartitions_right)
    
        query = df.merge(df2)
>       query["new"] = query.b + query.c

dask/dataframe/dask_expr/tests/test_merge.py:243: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b, c, d, e, f, x]
Index: [], key = 'new'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_merge_combine_similar_intermediate_projections ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_merge_combine_similar_intermediate_projections():
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                "b": 1,
                "c": 1,
            }
        )
        pdf2 = pd.DataFrame({"a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "x": 1})
        pdf3 = pd.DataFrame({"d": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "e": 1, "y": 1})
    
        df = from_pandas(pdf, npartitions=2)
        df2 = from_pandas(pdf2, npartitions=3)
        df3 = from_pandas(pdf3, npartitions=3)
    
        q = df.merge(df2).merge(df3, left_on="b", right_on="d")[["b", "x", "y"]]
>       q["new"] = q.b + q.x

dask/dataframe/dask_expr/tests/test_merge.py:271: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [b, x, y]
Index: [], key = 'new'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ test_categorical_merge_with_merge_column_cat_in_one_and_not_other_upcasts ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_categorical_merge_with_merge_column_cat_in_one_and_not_other_upcasts():
        df1 = pd.DataFrame({"A": pd.Categorical([0, 1]), "B": pd.Categorical(["a", "b"])})
        df2 = pd.DataFrame({"C": pd.Categorical(["a", "b"])})
    
>       expected = pd.merge(df2, df1, left_index=True, right_on="A")

dask/dataframe/dask_expr/tests/test_merge.py:290: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:170: in merge
    op = _MergeOperation(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:807: in __init__
    self._maybe_coerce_merge_keys()
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1552: in _maybe_coerce_merge_keys
    self.right[name] = self.right[name].astype(typ)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    A  B
0  0  a
1  1  b, key = 'A'
value = 0    0
1    1
Name: A, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_merge_empty_left_df[right] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right'

    @pytest.mark.parametrize("how", ["right", "outer"])
    def test_merge_empty_left_df(how):
        left = pd.DataFrame({"a": [1, 1, 2, 2], "val": [5, 6, 7, 8]})
        right = pd.DataFrame({"a": [0, 0, 3, 3], "val": [11, 12, 13, 14]})
    
        dd_left = from_pandas(left, npartitions=4)
        dd_right = from_pandas(right, npartitions=4)
    
        merged = dd_left.merge(dd_right, on="a", how=how)
>       expected = left.merge(right, on="a", how=how)

dask/dataframe/dask_expr/tests/test_merge.py:523: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     a  val_x  val_y
0 NaN    NaN     11
1 NaN    NaN     12
2 NaN    NaN     13
3 NaN    NaN     14
key = 'a', value = 0    0
1    0
2    3
3    3
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_merge_empty_left_df[outer] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'outer'

    @pytest.mark.parametrize("how", ["right", "outer"])
    def test_merge_empty_left_df(how):
        left = pd.DataFrame({"a": [1, 1, 2, 2], "val": [5, 6, 7, 8]})
        right = pd.DataFrame({"a": [0, 0, 3, 3], "val": [11, 12, 13, 14]})
    
        dd_left = from_pandas(left, npartitions=4)
        dd_right = from_pandas(right, npartitions=4)
    
        merged = dd_left.merge(dd_right, on="a", how=how)
>       expected = left.merge(right, on="a", how=how)

dask/dataframe/dask_expr/tests/test_merge.py:523: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      a  val_x  val_y
0  NaN    NaN   11.0
1  NaN    NaN   12.0
2  1.0    5.0    NaN
3  1.0    6.0    NaN
4  2.0    7.0    NaN
5  2.0    8.0    NaN
6  NaN    NaN   13.0
7  NaN    NaN   14.0
key = 'a'
value = 0    0
1    0
2    1
3    1
4    2
5    2
6    3
7    3
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_merge_columns_dtypes1[True-inner] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'inner', on_index = True

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    @pytest.mark.parametrize("on_index", [True, False])
    def test_merge_columns_dtypes1(how, on_index):
        # tests results of merges with merge columns having different dtypes;
        # asserts that either the merge was successful or the corresponding warning is raised
        # addresses issue #4574
    
        df1 = pd.DataFrame(
            {"A": list(np.arange(5).astype(float)) * 2, "B": list(np.arange(5)) * 2}
        )
        df2 = pd.DataFrame({"A": np.arange(5), "B": np.arange(5)})
    
        a = from_pandas(df1, 2)  # merge column "A" is float
        b = from_pandas(df2, 2)  # merge column "A" is int
    
        on = ["A"]
        left_index = right_index = on_index
    
        if on_index:
            a = a.set_index("A")
            b = b.set_index("A")
            on = None
    
        with warnings.catch_warnings(record=True) as record:
            warnings.simplefilter("always")
            result = merge(
                a, b, on=on, how=how, left_index=left_index, right_index=right_index
            )
            warned = any("merge column data type mismatches" in str(r) for r in record)
    
        # result type depends on merge operation -> convert to pandas
>       result = result if isinstance(result, pd.DataFrame) else result.compute()

dask/dataframe/dask_expr/tests/test_merge.py:592: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_merge_columns_dtypes1[True-outer] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'outer', on_index = True

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    @pytest.mark.parametrize("on_index", [True, False])
    def test_merge_columns_dtypes1(how, on_index):
        # tests results of merges with merge columns having different dtypes;
        # asserts that either the merge was successful or the corresponding warning is raised
        # addresses issue #4574
    
        df1 = pd.DataFrame(
            {"A": list(np.arange(5).astype(float)) * 2, "B": list(np.arange(5)) * 2}
        )
        df2 = pd.DataFrame({"A": np.arange(5), "B": np.arange(5)})
    
        a = from_pandas(df1, 2)  # merge column "A" is float
        b = from_pandas(df2, 2)  # merge column "A" is int
    
        on = ["A"]
        left_index = right_index = on_index
    
        if on_index:
            a = a.set_index("A")
            b = b.set_index("A")
            on = None
    
        with warnings.catch_warnings(record=True) as record:
            warnings.simplefilter("always")
            result = merge(
                a, b, on=on, how=how, left_index=left_index, right_index=right_index
            )
            warned = any("merge column data type mismatches" in str(r) for r in record)
    
        # result type depends on merge operation -> convert to pandas
>       result = result if isinstance(result, pd.DataFrame) else result.compute()

dask/dataframe/dask_expr/tests/test_merge.py:592: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_merge_columns_dtypes1[True-left] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'left', on_index = True

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    @pytest.mark.parametrize("on_index", [True, False])
    def test_merge_columns_dtypes1(how, on_index):
        # tests results of merges with merge columns having different dtypes;
        # asserts that either the merge was successful or the corresponding warning is raised
        # addresses issue #4574
    
        df1 = pd.DataFrame(
            {"A": list(np.arange(5).astype(float)) * 2, "B": list(np.arange(5)) * 2}
        )
        df2 = pd.DataFrame({"A": np.arange(5), "B": np.arange(5)})
    
        a = from_pandas(df1, 2)  # merge column "A" is float
        b = from_pandas(df2, 2)  # merge column "A" is int
    
        on = ["A"]
        left_index = right_index = on_index
    
        if on_index:
            a = a.set_index("A")
            b = b.set_index("A")
            on = None
    
        with warnings.catch_warnings(record=True) as record:
            warnings.simplefilter("always")
            result = merge(
                a, b, on=on, how=how, left_index=left_index, right_index=right_index
            )
            warned = any("merge column data type mismatches" in str(r) for r in record)
    
        # result type depends on merge operation -> convert to pandas
>       result = result if isinstance(result, pd.DataFrame) else result.compute()

dask/dataframe/dask_expr/tests/test_merge.py:592: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_merge_columns_dtypes1[True-right] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', on_index = True

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    @pytest.mark.parametrize("on_index", [True, False])
    def test_merge_columns_dtypes1(how, on_index):
        # tests results of merges with merge columns having different dtypes;
        # asserts that either the merge was successful or the corresponding warning is raised
        # addresses issue #4574
    
        df1 = pd.DataFrame(
            {"A": list(np.arange(5).astype(float)) * 2, "B": list(np.arange(5)) * 2}
        )
        df2 = pd.DataFrame({"A": np.arange(5), "B": np.arange(5)})
    
        a = from_pandas(df1, 2)  # merge column "A" is float
        b = from_pandas(df2, 2)  # merge column "A" is int
    
        on = ["A"]
        left_index = right_index = on_index
    
        if on_index:
            a = a.set_index("A")
            b = b.set_index("A")
            on = None
    
        with warnings.catch_warnings(record=True) as record:
            warnings.simplefilter("always")
            result = merge(
                a, b, on=on, how=how, left_index=left_index, right_index=right_index
            )
            warned = any("merge column data type mismatches" in str(r) for r in record)
    
        # result type depends on merge operation -> convert to pandas
>       result = result if isinstance(result, pd.DataFrame) else result.compute()

dask/dataframe/dask_expr/tests/test_merge.py:592: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_avoid_overeager_filter_pushdown __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_merge_avoid_overeager_filter_pushdown():
        df = pd.DataFrame({"a": [1, 2, 3], "b": 1})
        ddf = from_pandas(df, npartitions=2)
        df2 = pd.DataFrame({"a": [2, 3, 4], "c": 1})
        ddf2 = from_pandas(df2, npartitions=2)
        merged = ddf.merge(ddf2, on="a", how="left")
        rhs = merged.c.sum()
>       q = merged[merged.a > 1].assign(c=rhs)

dask/dataframe/dask_expr/tests/test_merge.py:836: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b, c]
Index: [], key = 'c'
value = np.int64(1)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_mismatching_meta _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_mismatching_meta():
        df1 = from_pandas(pd.DataFrame({"value": [1, 2, 3]}))
    
        def creator(i):
            return pd.DataFrame(
                {"value2": [1, 2, 3]}, index=pd.Index([1, 2, 3], name="index")
            )
    
        df2 = from_delayed(
            [delayed(creator)(i) for i in range(3)],
            meta=pd.DataFrame({"value2": [1, 2, 3]}),
        )
        result = df1.join(df2)
>       assert_eq(result, df1.compute().join(df2.compute()), check_index=False)

dask/dataframe/dask_expr/tests/test_merge.py:1072: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
               value value2
npartitions=3              
               int64  int64
        ... ...    ...
                 ...    ...
                 ...    ...
Dask Name: merge, 4 expressions
Expr=Merge(bb43fe3)
b =    value  value2
0      1     NaN
1      2     1.0
1      2     1.0
1      2     1.0
2      3     2.0
2      3     2.0
2      3     2.0
serialize_graph = True, args = (), kwargs = {'check_index': False}
obj = Dask DataFrame Structure:
               value value2
npartitions=3              
               int64  int64
        ... ...    ...
                 ...    ...
                 ...    ...
Dask Name: merge, 4 expressions
Expr=Merge(bb43fe3)

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_mismatching_meta.<locals>.creator at 0x7f5bd80e3ab0>
E                           when serializing tuple item 6
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item 'creator-5f5d7c48-5c8f-49e5-bf23-5d7fcc363d0d'

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_________________ test_avoid_shuffle_when_possible[<lambda>6] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = <function <lambda> at 0x7f5bf4f6c9e0>

    @pytest.mark.parametrize(
        "func",
        [
            lambda x: x["a"],
            lambda x: x[x.a > 5],
            lambda x: x.drop(columns=["a"]),
            lambda x: x.drop_duplicates(),
            lambda x: x.dropna(),
            lambda x: x.rename(columns={"a": "x"}),
            lambda x: x.assign(z=x.a + x.b),
            lambda x: x.assign(b=x.a + x.b),
        ],
    )
    def test_avoid_shuffle_when_possible(func):
        pdf = pd.DataFrame({"a": [1, 2, 3, 4, 5, 6] * 100, "b": 1, "c": 2})
    
        df = from_pandas(pdf, npartitions=4)
        q = df.groupby("a").sum(split_out=True).reset_index()
>       q = func(q)

dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py:92: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py:83: in <lambda>
    lambda x: x.assign(z=x.a + x.b),
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b, c]
Index: [], key = 'z'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_avoid_shuffle_when_possible[<lambda>7] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = <function <lambda> at 0x7f5bf4f6ca90>

    @pytest.mark.parametrize(
        "func",
        [
            lambda x: x["a"],
            lambda x: x[x.a > 5],
            lambda x: x.drop(columns=["a"]),
            lambda x: x.drop_duplicates(),
            lambda x: x.dropna(),
            lambda x: x.rename(columns={"a": "x"}),
            lambda x: x.assign(z=x.a + x.b),
            lambda x: x.assign(b=x.a + x.b),
        ],
    )
    def test_avoid_shuffle_when_possible(func):
        pdf = pd.DataFrame({"a": [1, 2, 3, 4, 5, 6] * 100, "b": 1, "c": 2})
    
        df = from_pandas(pdf, npartitions=4)
        q = df.groupby("a").sum(split_out=True).reset_index()
>       q = func(q)

dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py:92: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py:84: in <lambda>
    lambda x: x.assign(b=x.a + x.b),
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b, c]
Index: [], key = 'b'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_reduction_method _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_reduction_method():
        df = pd.DataFrame({"x": range(50), "y": range(50, 100)})
        ddf = from_pandas(df, npartitions=4)
    
        chunk = lambda x, val=0: (x >= val).sum()
        agg = lambda x: x.sum()
    
        # Output of chunk is a scalar
        res = ddf.x.reduction(chunk, aggregate=agg)
>       assert_eq(res, df.x.count())

dask/dataframe/dask_expr/tests/test_reductions.py:450: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = <dask_expr.expr.Scalar: expr=df['x'].customreduction(meta=<no_default>, chunk_kwargs={'func': <function test_reduction...8968d50>}, combine_kwargs={'func': <function test_reduction_method.<locals>.<lambda> at 0x7f5bb8968d50>}), dtype=int64>
b = np.int64(50), serialize_graph = True, args = (), kwargs = {}
obj = <dask_expr.expr.Scalar: expr=df['x'].customreduction(meta=<no_default>, chunk_kwargs={'func': <function test_reduction...8968d50>}, combine_kwargs={'func': <function test_reduction_method.<locals>.<lambda> at 0x7f5bb8968d50>}), dtype=int64>

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_reduction_method.<locals>.<lambda> at 0x7f5bb8968d50>
E                           when serializing dict item 'func'
E                           when serializing tuple item 3
E                           when serializing dict item ('customreduction-tree-fde7fcc6d70aeeb4450629b0f4e9ff12', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_______________________ test_reduction_split_every_false _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_reduction_split_every_false():
        pdf = pd.DataFrame({"a": [1]})
        df = from_pandas(pdf, npartitions=1)
        result = df.reduction(chunk=lambda x: x, split_every=False)
>       assert_eq(result, pdf)

dask/dataframe/dask_expr/tests/test_reductions.py:529: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   a
npartitions=1       
               int64
                 ...
Dask Nam...ne_kwargs={'func': <function test_reduction_split_every_false.<locals>.<lambda> at 0x7f5bb89685c0>}, split_every=False)
b =    a
0  1, serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   a
npartitions=1       
               int64
                 ...
Dask Nam...ne_kwargs={'func': <function test_reduction_split_every_false.<locals>.<lambda> at 0x7f5bb89685c0>}, split_every=False)

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_reduction_split_every_false.<locals>.<lambda> at 0x7f5bb89685c0>
E                           when serializing dict item 'func'
E                           when serializing tuple item 3
E                           when serializing dict item ('customreduction-tree-37b8edea357b8be5a12cd00e55800e04', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
________________ test_cat_value_counts_large_unknown_categories ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_cat_value_counts_large_unknown_categories():
        pdf = pd.DataFrame({"x": np.random.randint(1, 1_000_000, (250_000,))})
        df = from_pandas(pdf, npartitions=50)
>       df["x"] = df["x"].astype("category")

dask/dataframe/dask_expr/tests/test_reductions.py:544: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'x'
value = Series([], Name: x, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_repartition_combine_similar[kwargs0] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

kwargs = {'npartitions': 2}

    @pytest.mark.parametrize(
        "kwargs",
        [
            {"npartitions": 2},
            {"npartitions": 4},
            {"divisions": (0, 1, 79)},
            {"partition_size": "1kb"},
        ],
    )
    def test_repartition_combine_similar(kwargs):
        pdf = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6, 7, 8] * 10, "y": 1, "z": 2})
        df = from_pandas(pdf, npartitions=3)
        query = df.repartition(**kwargs)
>       query["new"] = query.x + query.y

dask/dataframe/dask_expr/tests/test_repartition.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = 'new'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_repartition_combine_similar[kwargs1] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

kwargs = {'npartitions': 4}

    @pytest.mark.parametrize(
        "kwargs",
        [
            {"npartitions": 2},
            {"npartitions": 4},
            {"divisions": (0, 1, 79)},
            {"partition_size": "1kb"},
        ],
    )
    def test_repartition_combine_similar(kwargs):
        pdf = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6, 7, 8] * 10, "y": 1, "z": 2})
        df = from_pandas(pdf, npartitions=3)
        query = df.repartition(**kwargs)
>       query["new"] = query.x + query.y

dask/dataframe/dask_expr/tests/test_repartition.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = 'new'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_repartition_combine_similar[kwargs2] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

kwargs = {'divisions': (0, 1, 79)}

    @pytest.mark.parametrize(
        "kwargs",
        [
            {"npartitions": 2},
            {"npartitions": 4},
            {"divisions": (0, 1, 79)},
            {"partition_size": "1kb"},
        ],
    )
    def test_repartition_combine_similar(kwargs):
        pdf = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6, 7, 8] * 10, "y": 1, "z": 2})
        df = from_pandas(pdf, npartitions=3)
        query = df.repartition(**kwargs)
>       query["new"] = query.x + query.y

dask/dataframe/dask_expr/tests/test_repartition.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = 'new'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_repartition_combine_similar[kwargs3] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

kwargs = {'partition_size': '1kb'}

    @pytest.mark.parametrize(
        "kwargs",
        [
            {"npartitions": 2},
            {"npartitions": 4},
            {"divisions": (0, 1, 79)},
            {"partition_size": "1kb"},
        ],
    )
    def test_repartition_combine_similar(kwargs):
        pdf = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6, 7, 8] * 10, "y": 1, "z": 2})
        df = from_pandas(pdf, npartitions=3)
        query = df.repartition(**kwargs)
>       query["new"] = query.x + query.y

dask/dataframe/dask_expr/tests/test_repartition.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = 'new'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_pivot_table[first] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                   x                y      z      a
npartitions=3                           ...      ...    ...    ...
5                ...              ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =    x  y   z  a
0  1  4   4  1
1  2  5  15  1
2  3  8   8  1
3  4  6  16  1
4  5  1   1  1
5  6  4  14  1
aggfunc = 'first'

    @pytest.mark.parametrize("aggfunc", ["first", "last", "sum", "mean", "count"])
    def test_pivot_table(df, pdf, aggfunc):
        assert_eq(
            df.pivot_table(index="x", columns="y", values="z", aggfunc=aggfunc),
            pdf.pivot_table(
                index="x", columns="y", values="z", aggfunc=aggfunc, observed=False
            ),
            check_dtype=aggfunc != "count",
        )
    
        assert_eq(
            df.pivot_table(index="x", columns="y", values=["z", "a"], aggfunc=aggfunc),
            pdf.pivot_table(
                index="x", columns="y", values=["z", "a"], aggfunc=aggfunc, observed=False
            ),
            check_dtype=aggfunc != "count",
        )
    
        assert_eq(
>           pivot_table(df, index="x", columns="y", values=["z", "a"], aggfunc=aggfunc),
            pdf.pivot_table(
                index="x", columns="y", values=["z", "a"], aggfunc=aggfunc, observed=False
            ),
            check_dtype=aggfunc != "count",
        )

dask/dataframe/dask_expr/tests/test_reshape.py:49: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:6039: in pivot_table
    return new_collection(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_reductions.py:699: in _meta
    meta[value_col] = meta[value_col].astype(
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4301: in __setitem__
    self._set_item_frame_value(key, value)
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4441: in _set_item_frame_value
    self[col] = value[col_droplevel]
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [(a, 1), (a, 4), (a, 5), (a, 6), (a, 8), (z, 1), (z, 4), (z, 5), (z, 6), (z, 8)]
Index: []
key = ('z', 4), value = Series([], Name: 4, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_pivot_table[last] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                   x                y      z      a
npartitions=3                           ...      ...    ...    ...
5                ...              ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =    x  y   z  a
0  1  4   4  1
1  2  5  15  1
2  3  8   8  1
3  4  6  16  1
4  5  1   1  1
5  6  4  14  1
aggfunc = 'last'

    @pytest.mark.parametrize("aggfunc", ["first", "last", "sum", "mean", "count"])
    def test_pivot_table(df, pdf, aggfunc):
        assert_eq(
            df.pivot_table(index="x", columns="y", values="z", aggfunc=aggfunc),
            pdf.pivot_table(
                index="x", columns="y", values="z", aggfunc=aggfunc, observed=False
            ),
            check_dtype=aggfunc != "count",
        )
    
        assert_eq(
>           df.pivot_table(index="x", columns="y", values=["z", "a"], aggfunc=aggfunc),
            pdf.pivot_table(
                index="x", columns="y", values=["z", "a"], aggfunc=aggfunc, observed=False
            ),
            check_dtype=aggfunc != "count",
        )

dask/dataframe/dask_expr/tests/test_reshape.py:41: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3703: in pivot_table
    return pivot_table(self, index, columns, values, aggfunc)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:6039: in pivot_table
    return new_collection(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_reductions.py:699: in _meta
    meta[value_col] = meta[value_col].astype(
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4301: in __setitem__
    self._set_item_frame_value(key, value)
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4441: in _set_item_frame_value
    self[col] = value[col_droplevel]
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [(a, 1), (a, 4), (a, 5), (a, 6), (a, 8), (z, 1), (z, 4), (z, 5), (z, 6), (z, 8)]
Index: []
key = ('z', 4), value = Series([], Name: 4, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_pivot_table_fails ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                   x                y      z      a
npartitions=3                           ...      ...    ...    ...
5                ...              ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df

    def test_pivot_table_fails(df):
        with pytest.raises(ValueError, match="must be the name of an existing column"):
            df.pivot_table(index="aaa", columns="y", values="z")
        with pytest.raises(ValueError, match="must be the name of an existing column"):
            df.pivot_table(index=["a"], columns="y", values="z")
    
        with pytest.raises(ValueError, match="must be the name of an existing column"):
            df.pivot_table(index="a", columns="xxx", values="z")
        with pytest.raises(ValueError, match="must be the name of an existing column"):
            df.pivot_table(index="a", columns=["x"], values="z")
    
        with pytest.raises(ValueError, match="'columns' must be category dtype"):
            df.pivot_table(index="a", columns="x", values="z")
    
        df2 = df.copy()
>       df2["y"] = df2.y.cat.as_unknown()

dask/dataframe/dask_expr/tests/test_reshape.py:72: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z, a]
Index: [], key = 'y'
value = Series([], Name: y, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_rolling_agg[1-1] _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    def test_rolling_agg(df, pdf, window):
        def my_sum(vals, foo=None, *, bar=None):
            return vals.sum()
    
        result = df.rolling(window).agg(my_sum, "foo", bar="bar")
        expected = pdf.rolling(window).agg(my_sum, "foo", bar="bar")
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:89: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...pe': None}, how_args=(<function test_rolling_agg.<locals>.my_sum at 0x7f5babafefb0>, 'foo'), how_kwargs={'bar': 'bar'})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...pe': None}, how_args=(<function test_rolling_agg.<locals>.my_sum at 0x7f5babafefb0>, 'foo'), how_kwargs={'bar': 'bar'})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_agg.<locals>.my_sum at 0x7f5babafefb0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-2d3acd409b98e0291e2c9ab237abd7b9', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
____________________________ test_rolling_agg[1-2] _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    def test_rolling_agg(df, pdf, window):
        def my_sum(vals, foo=None, *, bar=None):
            return vals.sum()
    
        result = df.rolling(window).agg(my_sum, "foo", bar="bar")
        expected = pdf.rolling(window).agg(my_sum, "foo", bar="bar")
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:89: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...pe': None}, how_args=(<function test_rolling_agg.<locals>.my_sum at 0x7f5babafe610>, 'foo'), how_kwargs={'bar': 'bar'})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...pe': None}, how_args=(<function test_rolling_agg.<locals>.my_sum at 0x7f5babafe610>, 'foo'), how_kwargs={'bar': 'bar'})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_agg.<locals>.my_sum at 0x7f5babafe610>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-72c45fb96f5fa9033fcccd14f359652f', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
____________________________ test_rolling_agg[2-1] _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    def test_rolling_agg(df, pdf, window):
        def my_sum(vals, foo=None, *, bar=None):
            return vals.sum()
    
        result = df.rolling(window).agg(my_sum, "foo", bar="bar")
        expected = pdf.rolling(window).agg(my_sum, "foo", bar="bar")
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:89: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...pe': None}, how_args=(<function test_rolling_agg.<locals>.my_sum at 0x7f5babafd900>, 'foo'), how_kwargs={'bar': 'bar'})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...pe': None}, how_args=(<function test_rolling_agg.<locals>.my_sum at 0x7f5babafd900>, 'foo'), how_kwargs={'bar': 'bar'})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_agg.<locals>.my_sum at 0x7f5babafd900>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-caa436b8089057a8f65b1d32b851fdf0', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
____________________________ test_rolling_agg[2-2] _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    def test_rolling_agg(df, pdf, window):
        def my_sum(vals, foo=None, *, bar=None):
            return vals.sum()
    
        result = df.rolling(window).agg(my_sum, "foo", bar="bar")
        expected = pdf.rolling(window).agg(my_sum, "foo", bar="bar")
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:89: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...pe': None}, how_args=(<function test_rolling_agg.<locals>.my_sum at 0x7f5babafe400>, 'foo'), how_kwargs={'bar': 'bar'})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...pe': None}, how_args=(<function test_rolling_agg.<locals>.my_sum at 0x7f5babafe400>, 'foo'), how_kwargs={'bar': 'bar'})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_agg.<locals>.my_sum at 0x7f5babafe400>
E                           when serializing tuple item 0
E                           when serializing dict item 'how_args'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_overlap_chunk-9d29e08a4f843764f3b0dc23c5c58b93', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_______________________ test_rolling_apply[2-1-True-1-1] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = True, foo = 1, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...test_rolling_apply.<locals>.my_sum at 0x7f5babafc1a0>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...test_rolling_apply.<locals>.my_sum at 0x7f5babafc1a0>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babafc1a0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-166110342713781e47d59fdb1f385c98', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_______________________ test_rolling_apply[2-1-True-1-2] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = True, foo = 1, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...test_rolling_apply.<locals>.my_sum at 0x7f5babaffed0>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...test_rolling_apply.<locals>.my_sum at 0x7f5babaffed0>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babaffed0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-26c80b362f5f44166ec4340404298b5a', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_______________________ test_rolling_apply[2-1-True-2-1] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = True, foo = 1, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...test_rolling_apply.<locals>.my_sum at 0x7f5babaff8a0>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...test_rolling_apply.<locals>.my_sum at 0x7f5babaff8a0>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babaff8a0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-41ceaef7ce49c1842b01874fd9789d7f', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_______________________ test_rolling_apply[2-1-True-2-2] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = True, foo = 1, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...test_rolling_apply.<locals>.my_sum at 0x7f5babaff690>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...test_rolling_apply.<locals>.my_sum at 0x7f5babaff690>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babaff690>
E                           when serializing tuple item 0
E                           when serializing dict item 'how_args'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_overlap_chunk-16ff5053822778e61ff4f98ab6d28598', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
______________________ test_rolling_apply[2-1-False-1-1] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = False, foo = 1, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...est_rolling_apply.<locals>.my_sum at 0x7f5bb89dd9b0>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...est_rolling_apply.<locals>.my_sum at 0x7f5bb89dd9b0>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5bb89dd9b0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-18882178daecc616a0a23fecfd0bb22a', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
______________________ test_rolling_apply[2-1-False-1-2] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = False, foo = 1, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...est_rolling_apply.<locals>.my_sum at 0x7f5babb2c300>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...est_rolling_apply.<locals>.my_sum at 0x7f5babb2c300>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2c300>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-37b918886750a20dc2978b612aa3a596', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
______________________ test_rolling_apply[2-1-False-2-1] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = False, foo = 1, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...est_rolling_apply.<locals>.my_sum at 0x7f5babb2c040>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...est_rolling_apply.<locals>.my_sum at 0x7f5babb2c040>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2c040>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-e8742a23cd4536cf2eb4f452cb59bb0b', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
______________________ test_rolling_apply[2-1-False-2-2] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = False, foo = 1, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...est_rolling_apply.<locals>.my_sum at 0x7f5babb2c670>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...est_rolling_apply.<locals>.my_sum at 0x7f5babb2c670>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2c670>
E                           when serializing tuple item 0
E                           when serializing dict item 'how_args'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_overlap_chunk-cc787228e41e72b0011c87f5d2d1e8fa', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[2-None-True-1-1] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = True, foo = None, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2c880>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2c880>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2c880>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-a14ebeabbd63113624d919882a01507f', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[2-None-True-1-2] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = True, foo = None, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2ca90>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2ca90>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2ca90>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-cfc58d2d1c28f1cad031b3e400146040', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[2-None-True-2-1] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = True, foo = None, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2cca0>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2cca0>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2cca0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-4ae8d7f349151df6f4f851eeb795b0b0', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[2-None-True-2-2] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = True, foo = None, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2ceb0>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2ceb0>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2ceb0>
E                           when serializing tuple item 0
E                           when serializing dict item 'how_args'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_overlap_chunk-9c573707bdc56fe18e665ff24db78bdf', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[2-None-False-1-1] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = False, foo = None, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2d0c0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2d0c0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2d0c0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-ba5cc92c12bef659031336a4e8242db7', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[2-None-False-1-2] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = False, foo = None, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2d2d0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2d2d0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2d2d0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-bbe3001785dc9886f6c7c17306a98532', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[2-None-False-2-1] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = False, foo = None, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2d4e0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2d4e0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2d4e0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-b3442bf4f9b3bc4db420e8236bcb53ab', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[2-None-False-2-2] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = False, foo = None, bar = 2

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2d6f0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': 2}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2d6f0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': 2}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2d6f0>
E                           when serializing tuple item 0
E                           when serializing dict item 'how_args'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_overlap_chunk-846380148274fb4c50f901b91360408c', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[None-1-True-1-1] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = True, foo = 1, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2d900>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2d900>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2d900>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-5f6bddb32d7918da8de307f854a80dfb', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[None-1-True-1-2] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = True, foo = 1, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2db10>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2db10>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2db10>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-6d30bfb05cd5b101792f111324b5e6eb', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[None-1-True-2-1] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = True, foo = 1, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2e090>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2e090>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2e090>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-4fc6a0728d09712e8706968f0d9d6f84', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[None-1-True-2-2] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = True, foo = 1, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2e350>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...t_rolling_apply.<locals>.my_sum at 0x7f5babb2e350>,), how_kwargs={'raw': True, 'args': (1,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2e350>
E                           when serializing tuple item 0
E                           when serializing dict item 'how_args'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_overlap_chunk-57dea7e1c1e398d790805f0ebefc885c', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[None-1-False-1-1] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = False, foo = 1, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2d380>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2d380>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2d380>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-8b061141f80f5b6e20d59f76c70067c8', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[None-1-False-1-2] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = False, foo = 1, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2e4b0>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2e4b0>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2e4b0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-40c3b9521bcb1590b7866e5e672dd9c2', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[None-1-False-2-1] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = False, foo = 1, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2e6c0>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2e6c0>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2e6c0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-b40bcf192c3b026d18368a64ba0aeda2', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________ test_rolling_apply[None-1-False-2-2] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = False, foo = 1, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2e8d0>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00..._rolling_apply.<locals>.my_sum at 0x7f5babb2e8d0>,), how_kwargs={'raw': False, 'args': (1,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2e8d0>
E                           when serializing tuple item 0
E                           when serializing dict item 'how_args'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_overlap_chunk-487373152360878548f641fbe1da4891', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
____________________ test_rolling_apply[None-None-True-1-1] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = True, foo = None, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...olling_apply.<locals>.my_sum at 0x7f5babb2eae0>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...olling_apply.<locals>.my_sum at 0x7f5babb2eae0>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2eae0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-848348b673bd501b152e1296b3b3744b', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
____________________ test_rolling_apply[None-None-True-1-2] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = True, foo = None, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...olling_apply.<locals>.my_sum at 0x7f5babb2ecf0>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...olling_apply.<locals>.my_sum at 0x7f5babb2ecf0>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2ecf0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-dd7a7a54d2feb4ca83d5a592f4ae1270', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
____________________ test_rolling_apply[None-None-True-2-1] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = True, foo = None, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...olling_apply.<locals>.my_sum at 0x7f5babb2ef00>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...olling_apply.<locals>.my_sum at 0x7f5babb2ef00>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2ef00>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-2f742b3cd749e1e5cee385d8a5185c7d', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
____________________ test_rolling_apply[None-None-True-2-2] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = True, foo = None, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...olling_apply.<locals>.my_sum at 0x7f5babb2f110>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...olling_apply.<locals>.my_sum at 0x7f5babb2f110>,), how_kwargs={'raw': True, 'args': (None,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babb2f110>
E                           when serializing tuple item 0
E                           when serializing dict item 'how_args'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_overlap_chunk-79e4fbc8728ac5acab45533239b6bb18', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
___________________ test_rolling_apply[None-None-False-1-1] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = False, foo = None, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...lling_apply.<locals>.my_sum at 0x7f5babafcd50>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...lling_apply.<locals>.my_sum at 0x7f5babafcd50>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babafcd50>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-50eb349946cb5bd8d54481417bed4eea', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
___________________ test_rolling_apply[None-None-False-1-2] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=1                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = False, foo = None, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...lling_apply.<locals>.my_sum at 0x7f5babf203b0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=1                        
2000-01-01 00:00...lling_apply.<locals>.my_sum at 0x7f5babf203b0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5babf203b0>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-d06dc7ba18a703299aef39df7319a577', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
___________________ test_rolling_apply[None-None-False-2-1] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 1, raw = False, foo = None, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...lling_apply.<locals>.my_sum at 0x7f5bd80e2820>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   0.0  1.0
2000-01-01 00:01:00   1.0  1.0
2000-01-01 00:02:00   2.0...-01-01 00:08:00   8.0  1.0
2000-01-01 00:09:00   9.0  1.0
2000-01-01 00:10:00  10.0  1.0
2000-01-01 00:11:00  11.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...lling_apply.<locals>.my_sum at 0x7f5bd80e2820>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5bd80e2820>
E                           when serializing list item 0
E                           when serializing tuple item 4
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_rolling_agg-90d911254c2ba9bac1e9341a60ff98aa', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
___________________ test_rolling_apply[None-None-False-2-2] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=2                    
2000-01-01 00:00:00  int64  int64
2000-01-01 00:06:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1
window = 2, raw = False, foo = None, bar = None

    @pytest.mark.parametrize("window", (1, 2))
    @pytest.mark.parametrize("df", (1, 2), indirect=True)
    @pytest.mark.parametrize("raw", (True, False))
    @pytest.mark.parametrize("foo", (1, None))
    @pytest.mark.parametrize("bar", (2, None))
    def test_rolling_apply(df, pdf, window, raw, foo, bar):
        def my_sum(vals, foo_=None, *, bar_=None):
            assert foo_ == foo
            assert bar_ == bar
            if raw:
                assert isinstance(vals, np.ndarray)
            else:
                assert isinstance(vals, pd.Series)
            return vals.sum()
    
        kwargs = dict(raw=raw, args=(foo,), kwargs=dict(bar_=bar))
    
        result = df.rolling(window).apply(my_sum, **kwargs)
        expected = pdf.rolling(window).apply(my_sum, **kwargs)
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...lling_apply.<locals>.my_sum at 0x7f5bb89694e0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': None}})
b =                       foo  bar
2000-01-01 00:00:00   NaN  NaN
2000-01-01 00:01:00   1.0  2.0
2000-01-01 00:02:00   3.0...-01-01 00:08:00  15.0  2.0
2000-01-01 00:09:00  17.0  2.0
2000-01-01 00:10:00  19.0  2.0
2000-01-01 00:11:00  21.0  2.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=2                        
2000-01-01 00:00...lling_apply.<locals>.my_sum at 0x7f5bb89694e0>,), how_kwargs={'raw': False, 'args': (None,), 'kwargs': {'bar_': None}})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_apply.<locals>.my_sum at 0x7f5bb89694e0>
E                           when serializing tuple item 0
E                           when serializing dict item 'how_args'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_overlap_chunk-9614e4ae5da0582e270a1e51e4f3873a', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_________________ test_rolling_one_element_window_empty_after __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=3                    
2000-01-01 00:00:00  int...    ...
2000-01-01 00:00:16    ...    ...
2000-01-01 00:00:22    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:00:02    1    1
2000-01-01 00:00:04    2    ...2000-01-01 00:00:16    8    1
2000-01-01 00:00:18    9    1
2000-01-01 00:00:20   10    1
2000-01-01 00:00:22   11    1

    def test_rolling_one_element_window_empty_after(df, pdf):
        pdf.index = pd.date_range("2000-01-01", periods=12, freq="2s")
        df = from_pandas(pdf, npartitions=3)
        result = df.map_overlap(lambda x: x.rolling("1s").count(), before="1s", after="1s")
        expected = pdf.rolling("1s").count()
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_rolling.py:162: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                         foo      bar
npartitions=3                        
2000-01-01 00:00...:16      ...      ...
2000-01-01 00:00:22      ...      ...
Dask Name: lambda, 2 expressions
Expr=MapPartitions(lambda)
b =                      foo  bar
2000-01-01 00:00:00  1.0  1.0
2000-01-01 00:00:02  1.0  1.0
2000-01-01 00:00:04  1.0  1....2000-01-01 00:00:16  1.0  1.0
2000-01-01 00:00:18  1.0  1.0
2000-01-01 00:00:20  1.0  1.0
2000-01-01 00:00:22  1.0  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                         foo      bar
npartitions=3                        
2000-01-01 00:00...:16      ...      ...
2000-01-01 00:00:22      ...      ...
Dask Name: lambda, 2 expressions
Expr=MapPartitions(lambda)

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_rolling_one_element_window_empty_after.<locals>.<lambda> at 0x7f5bb8969c70>
E                           when serializing tuple item 1
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_overlap_chunk-a0b126fa230dbcfaf93dcd3321694b1f', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
________________________ test_task_shuffle_index[32-3] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 3, max_branch = 32
pdf =      y
x     
0    0
1    1
2    2
3    3
4    4
..  ..
15  95
16  96
17  97
18  98
19  99

[100 rows x 1 columns]

    @pytest.mark.parametrize("npartitions", [3, 12])
    @pytest.mark.parametrize("max_branch", [32, 8])
    def test_task_shuffle_index(npartitions, max_branch, pdf):
        pdf = pdf.set_index("x")
        df = from_pandas(pdf, 10)
    
        df2 = df.shuffle(
            "x",
            shuffle_method="tasks",
            npartitions=npartitions,
            max_branch=max_branch,
        )
    
        # Check that the output partition count is correct
        assert df2.npartitions == (npartitions or df.npartitions)
    
        # Check the computed (re-ordered) result
>       assert_eq(df, df2, check_divisions=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_task_shuffle_index[32-12] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 12, max_branch = 32
pdf =      y
x     
0    0
1    1
2    2
3    3
4    4
..  ..
15  95
16  96
17  97
18  98
19  99

[100 rows x 1 columns]

    @pytest.mark.parametrize("npartitions", [3, 12])
    @pytest.mark.parametrize("max_branch", [32, 8])
    def test_task_shuffle_index(npartitions, max_branch, pdf):
        pdf = pdf.set_index("x")
        df = from_pandas(pdf, 10)
    
        df2 = df.shuffle(
            "x",
            shuffle_method="tasks",
            npartitions=npartitions,
            max_branch=max_branch,
        )
    
        # Check that the output partition count is correct
        assert df2.npartitions == (npartitions or df.npartitions)
    
        # Check the computed (re-ordered) result
>       assert_eq(df, df2, check_divisions=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_task_shuffle_index[8-3] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 3, max_branch = 8
pdf =      y
x     
0    0
1    1
2    2
3    3
4    4
..  ..
15  95
16  96
17  97
18  98
19  99

[100 rows x 1 columns]

    @pytest.mark.parametrize("npartitions", [3, 12])
    @pytest.mark.parametrize("max_branch", [32, 8])
    def test_task_shuffle_index(npartitions, max_branch, pdf):
        pdf = pdf.set_index("x")
        df = from_pandas(pdf, 10)
    
        df2 = df.shuffle(
            "x",
            shuffle_method="tasks",
            npartitions=npartitions,
            max_branch=max_branch,
        )
    
        # Check that the output partition count is correct
        assert df2.npartitions == (npartitions or df.npartitions)
    
        # Check the computed (re-ordered) result
>       assert_eq(df, df2, check_divisions=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_task_shuffle_index[8-12] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 12, max_branch = 8
pdf =      y
x     
0    0
1    1
2    2
3    3
4    4
..  ..
15  95
16  96
17  97
18  98
19  99

[100 rows x 1 columns]

    @pytest.mark.parametrize("npartitions", [3, 12])
    @pytest.mark.parametrize("max_branch", [32, 8])
    def test_task_shuffle_index(npartitions, max_branch, pdf):
        pdf = pdf.set_index("x")
        df = from_pandas(pdf, 10)
    
        df2 = df.shuffle(
            "x",
            shuffle_method="tasks",
            npartitions=npartitions,
            max_branch=max_branch,
        )
    
        # Check that the output partition count is correct
        assert df2.npartitions == (npartitions or df.npartitions)
    
        # Check the computed (re-ordered) result
>       assert_eq(df, df2, check_divisions=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_sort_values_[10-a-True] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

nelem = 10, by = 'a', ascending = True

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values_(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:200: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:272: in _layer
    for i in range(self.npartitions)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2156: in _divisions
    if self.ndim == 0:
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_sort_values_[10-b-False] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

nelem = 10, by = 'b', ascending = False

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values_(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:200: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:272: in _layer
    for i in range(self.npartitions)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2156: in _divisions
    if self.ndim == 0:
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_sort_values_[10-by2-True] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

nelem = 10, by = ['a', 'b'], ascending = True

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values_(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:200: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:272: in _layer
    for i in range(self.npartitions)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2156: in _divisions
    if self.ndim == 0:
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_sort_values_[500-a-True] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

nelem = 500, by = 'a', ascending = True

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values_(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:200: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:272: in _layer
    for i in range(self.npartitions)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2156: in _divisions
    if self.ndim == 0:
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_sort_values_[500-b-False] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

nelem = 500, by = 'b', ascending = False

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values_(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:200: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:272: in _layer
    for i in range(self.npartitions)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2156: in _divisions
    if self.ndim == 0:
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_sort_values_[500-by2-True] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

nelem = 500, by = ['a', 'b'], ascending = True

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values_(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:200: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:272: in _layer
    for i in range(self.npartitions)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2156: in _divisions
    if self.ndim == 0:
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_set_index[1.0-128000000.0] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]
upsample = 1.0, partition_size = 128000000.0

    @pytest.mark.parametrize("partition_size", [128e6, 128e5])
    @pytest.mark.parametrize("upsample", [1.0, 2.0])
    def test_set_index(df, pdf, upsample, partition_size):
>       assert_eq(
            df.set_index("x", upsample=upsample, partition_size=partition_size),
            pdf.set_index("x"),
        )

dask/dataframe/dask_expr/tests/test_shuffle.py:223: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_set_index[1.0-12800000.0] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]
upsample = 1.0, partition_size = 12800000.0

    @pytest.mark.parametrize("partition_size", [128e6, 128e5])
    @pytest.mark.parametrize("upsample", [1.0, 2.0])
    def test_set_index(df, pdf, upsample, partition_size):
>       assert_eq(
            df.set_index("x", upsample=upsample, partition_size=partition_size),
            pdf.set_index("x"),
        )

dask/dataframe/dask_expr/tests/test_shuffle.py:223: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_set_index[2.0-128000000.0] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]
upsample = 2.0, partition_size = 128000000.0

    @pytest.mark.parametrize("partition_size", [128e6, 128e5])
    @pytest.mark.parametrize("upsample", [1.0, 2.0])
    def test_set_index(df, pdf, upsample, partition_size):
>       assert_eq(
            df.set_index("x", upsample=upsample, partition_size=partition_size),
            pdf.set_index("x"),
        )

dask/dataframe/dask_expr/tests/test_shuffle.py:223: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_set_index[2.0-12800000.0] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]
upsample = 2.0, partition_size = 12800000.0

    @pytest.mark.parametrize("partition_size", [128e6, 128e5])
    @pytest.mark.parametrize("upsample", [1.0, 2.0])
    def test_set_index(df, pdf, upsample, partition_size):
>       assert_eq(
            df.set_index("x", upsample=upsample, partition_size=partition_size),
            pdf.set_index("x"),
        )

dask/dataframe/dask_expr/tests/test_shuffle.py:223: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_set_index_sorted _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_sorted(pdf):
        pdf = pdf.sort_values(by="y", ignore_index=True)
>       pdf["z"] = pdf["x"]

dask/dataframe/dask_expr/tests/test_shuffle.py:238: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]
key = 'z'
value = 0      0
1      1
2      2
3      3
4      4
      ..
95    15
96    16
97    17
98    18
99    19
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_set_index_pre_sorted ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_pre_sorted(pdf):
        pdf = pdf.sort_values(by="y", ignore_index=True)
>       pdf["z"] = pdf["x"]

dask/dataframe/dask_expr/tests/test_shuffle.py:258: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]
key = 'z'
value = 0      0
1      1
2      2
3      3
4      4
      ..
95    15
96    16
97    17
98    18
99    19
Name: x, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_set_index_no_sort[True-True] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

drop = True, append = True

    @pytest.mark.parametrize("drop", (True, False))
    @pytest.mark.parametrize("append", (True, False))
    def test_set_index_no_sort(drop, append):
        df = pd.DataFrame({"col1": [2, 4, 1, 3, 5], "col2": [1, 2, 3, 4, 5]})
        ddf = from_pandas(df, npartitions=2)
    
        assert ddf.npartitions > 1
    
        # Default is sort=True
        # Index in ddf will be same values, but sorted
        df_result = df.set_index("col1")
        ddf_result = ddf.set_index("col1")
        assert ddf_result.known_divisions
>       assert_eq(ddf_result, df_result.sort_index(), sort_results=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:287: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [col1, col2]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_set_index_no_sort[True-False] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

drop = False, append = True

    @pytest.mark.parametrize("drop", (True, False))
    @pytest.mark.parametrize("append", (True, False))
    def test_set_index_no_sort(drop, append):
        df = pd.DataFrame({"col1": [2, 4, 1, 3, 5], "col2": [1, 2, 3, 4, 5]})
        ddf = from_pandas(df, npartitions=2)
    
        assert ddf.npartitions > 1
    
        # Default is sort=True
        # Index in ddf will be same values, but sorted
        df_result = df.set_index("col1")
        ddf_result = ddf.set_index("col1")
        assert ddf_result.known_divisions
>       assert_eq(ddf_result, df_result.sort_index(), sort_results=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:287: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [col1, col2]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_set_index_no_sort[False-True] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

drop = True, append = False

    @pytest.mark.parametrize("drop", (True, False))
    @pytest.mark.parametrize("append", (True, False))
    def test_set_index_no_sort(drop, append):
        df = pd.DataFrame({"col1": [2, 4, 1, 3, 5], "col2": [1, 2, 3, 4, 5]})
        ddf = from_pandas(df, npartitions=2)
    
        assert ddf.npartitions > 1
    
        # Default is sort=True
        # Index in ddf will be same values, but sorted
        df_result = df.set_index("col1")
        ddf_result = ddf.set_index("col1")
        assert ddf_result.known_divisions
>       assert_eq(ddf_result, df_result.sort_index(), sort_results=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:287: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [col1, col2]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_set_index_no_sort[False-False] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

drop = False, append = False

    @pytest.mark.parametrize("drop", (True, False))
    @pytest.mark.parametrize("append", (True, False))
    def test_set_index_no_sort(drop, append):
        df = pd.DataFrame({"col1": [2, 4, 1, 3, 5], "col2": [1, 2, 3, 4, 5]})
        ddf = from_pandas(df, npartitions=2)
    
        assert ddf.npartitions > 1
    
        # Default is sort=True
        # Index in ddf will be same values, but sorted
        df_result = df.set_index("col1")
        ddf_result = ddf.set_index("col1")
        assert ddf_result.known_divisions
>       assert_eq(ddf_result, df_result.sort_index(), sort_results=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:287: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [col1, col2]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_set_index_repartition __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_repartition(df, pdf):
        result = df.set_index("x", npartitions=2)
        assert result.npartitions == 2
>       assert result.optimize(fuse=False).npartitions == 2

dask/dataframe/dask_expr/tests/test_shuffle.py:301: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_set_index_simplify ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_simplify(df, pdf):
>       q = df.set_index("x")["y"].optimize(fuse=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:306: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_set_index_numeric_columns ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_set_index_numeric_columns():
        pdf = pd.DataFrame(
            {
                0: list("ABAABBABAA"),
                1: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                2: [1, 2, 3, 2, 1, 3, 2, 4, 2, 3],
            }
        )
        ddf = from_pandas(pdf, 3)
>       assert_eq(ddf.set_index(0), pdf.set_index(0))

dask/dataframe/dask_expr/tests/test_shuffle.py:324: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0, 1, 2]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_sort_values[None] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]
shuffle = None

    @pytest.mark.parametrize("shuffle", [None, "tasks"])
    def test_sort_values(df, pdf, shuffle):
>       assert_eq(df.sort_values("x", shuffle_method=shuffle), pdf.sort_values("x"))

dask/dataframe/dask_expr/tests/test_shuffle.py:348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:272: in _layer
    for i in range(self.npartitions)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2156: in _divisions
    if self.ndim == 0:
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_sort_values[tasks] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]
shuffle = 'tasks'

    @pytest.mark.parametrize("shuffle", [None, "tasks"])
    def test_sort_values(df, pdf, shuffle):
>       assert_eq(df.sort_values("x", shuffle_method=shuffle), pdf.sort_values("x"))

dask/dataframe/dask_expr/tests/test_shuffle.py:348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:272: in _layer
    for i in range(self.npartitions)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2156: in _divisions
    if self.ndim == 0:
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:374: in _meta
    meta = self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_sort_values_optimize ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_sort_values_optimize(df, pdf):
>       q = df.sort_values("x")["y"].optimize(fuse=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1283: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________________ test_set_index_list ______________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_list(df, pdf):
>       assert_eq(df.set_index(["x"]), pdf.set_index(["x"]))

dask/dataframe/dask_expr/tests/test_shuffle.py:396: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_sort_values_descending __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_sort_values_descending(df, pdf):
>       assert_eq(
            df.sort_values(by="y", ascending=False),
            pdf.sort_values(by="y", ascending=False),
            sort_results=False,
        )

dask/dataframe/dask_expr/tests/test_shuffle.py:400: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:272: in _layer
    for i in range(self.npartitions)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2156: in _divisions
    if self.ndim == 0:
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_sort_head_nlargest_string ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

pdf =      x   y    z
0    0   0   a0
1    1   1   a1
2    2   2   a2
3    3   3   a3
4    4   4   a4
..  ..  ..  ...
95  15  95  a15
96  16  96  a16
97  17  97  a17
98  18  98  a18
99  19  99  a19

[100 rows x 3 columns]

    @xfail_gpu("cudf udf support")
    def test_sort_head_nlargest_string(pdf):
        pdf["z"] = "a" + pdf.x.map(str)
        df = from_pandas(pdf, npartitions=5)
        a = df.sort_values("z", ascending=False).head(10, compute=False)
>       assert_eq(a, pdf.sort_values("z", ascending=False).head(10))

dask/dataframe/dask_expr/tests/test_shuffle.py:515: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:272: in _layer
    for i in range(self.npartitions)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2584: in npartitions
    return len(self._divisions()) - 1
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2587: in _divisions
    return self.frame.divisions[: len(self._partitions) + 1]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2966: in _divisions
    divisions.append(self.frame.divisions[part])
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2156: in _divisions
    if self.ndim == 0:
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_set_index_head_nlargest _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_head_nlargest(df, pdf):
        a = df.set_index("x").head(10, compute=False)
        b = new_collection(NFirst(df, 10, _columns="x", ascending=True)).set_index("x")
        assert a.optimize()._name == b.optimize()._name
    
        a = df.set_index("x").tail(10, compute=False)
        b = new_collection(NLast(df, 10, _columns="x", ascending=True)).set_index("x")
        assert a.optimize()._name == b.optimize()._name
    
        # These still work, even if we haven't optimized them yet
>       df.set_index(df.x).head(3)

dask/dataframe/dask_expr/tests/test_shuffle.py:537: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:690: in head
    out = out.compute()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_set_index_head_nlargest_string ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

pdf =      x   y    z
0    0   0   a0
1    1   1   a1
2    2   2   a2
3    3   3   a3
4    4   4   a4
..  ..  ..  ...
95  15  95  a15
96  16  96  a16
97  17  97  a17
98  18  98  a18
99  19  99  a19

[100 rows x 3 columns]

    @pytest.mark.skipif(
        not pyarrow_strings_enabled() or not PANDAS_GE_220,
        reason="doesn't work without arrow",
    )
    @xfail_gpu("cudf udf support")
    def test_set_index_head_nlargest_string(pdf):
        pdf["z"] = "a" + pdf.x.map(str)
        df = from_pandas(pdf, npartitions=5)
        print(df.dtypes)
    
        a = df.set_index("z").head(10, compute=False)
>       assert_eq(a, pdf.set_index("z").sort_index().head(10))

dask/dataframe/dask_expr/tests/test_shuffle.py:552: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
----------------------------- Captured stdout call -----------------------------
x              int64
y              int64
z    string[pyarrow]
dtype: object
_______________________________ test_filter_sort _______________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df

    def test_filter_sort(df):
        a = df.sort_values("x")
        a = a[a.y > 40]
    
        b = df[df.y > 40]
        b = b.sort_values("x")
    
>       assert a.optimize()._name == b.optimize()._name

dask/dataframe/dask_expr/tests/test_shuffle.py:565: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________________ test_sort_values_add _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_sort_values_add():
        pdf = pd.DataFrame({"x": [1, 2, 3, 0, 1, 2, 4, 5], "y": 1})
        df = from_pandas(pdf, npartitions=2, sort=False)
        with dask.config.set({"dataframe.shuffle.method": "tasks"}):
            df = df.sort_values("x")
>           df["z"] = df.x + df.y

dask/dataframe/dask_expr/tests/test_shuffle.py:573: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = 'z'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_set_index_with_dask_dt_index[-16h] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

freq = '-16h'

    @pytest.mark.parametrize("freq", ["16h", "-16h"])
    def test_set_index_with_dask_dt_index(freq):
        values = {
            "x": [1, 2, 3, 4] * 3,
            "y": [10, 20, 30] * 4,
            "name": ["Alice", "Bob"] * 6,
        }
        date_index = pd.date_range(
            start="2022-02-22", freq=freq, periods=12
        ) - pd.Timedelta(seconds=30)
        df = pd.DataFrame(values, index=date_index)
        ddf = from_pandas(df, npartitions=3, sort=False)
        # specify a different date index entirely
        day_index = ddf.index.dt.floor("D")
        result = ddf.set_index(day_index)
>       assert_eq(result, pd.DataFrame(values, index=date_index.floor("D")))

dask/dataframe/dask_expr/tests/test_shuffle.py:608: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1939: in _meta
    args = [op._meta if isinstance(op, Expr) else op for op in self._args]
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, name]
Index: [], key = '_partitions'
value = DatetimeIndex([], dtype='datetime64[ns]', freq=None)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_set_index_sort_values_shuffle_options __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_sort_values_shuffle_options(df, pdf):
        q = df.set_index("x", shuffle_method="tasks", max_branch=10)
>       shuffle = list(q.optimize().find_operations(TaskShuffle))[0]

dask/dataframe/dask_expr/tests/test_shuffle.py:613: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_set_index_predicate_pushdown _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      y
x     
0    0
1    1
2    2
3    3
4    4
..  ..
15  95
16  96
17  97
18  98
19  99

[100 rows x 1 columns]

    def test_set_index_predicate_pushdown(df, pdf):
        pdf = pdf.set_index("x")
        query = df.set_index("x")
        result = query[query.y > 5]
        expected = pdf[pdf.y > 5]
>       assert_eq(result, expected)

dask/dataframe/dask_expr/tests/test_shuffle.py:628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_set_index_with_explicit_divisions ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_set_index_with_explicit_divisions():
        pdf = pd.DataFrame({"x": [4, 1, 2, 5]}, index=[10, 20, 30, 40])
    
        df = from_pandas(pdf, npartitions=2)
    
        result = df.set_index("x", divisions=[1, 3, 5])
        assert result.divisions == (1, 3, 5)
>       assert_eq(result, pdf.set_index("x"))

dask/dataframe/dask_expr/tests/test_shuffle.py:646: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_set_index_npartitions_changes ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_npartitions_changes(pdf):
        df = from_pandas(pdf, npartitions=30)
        result = df.set_index("x", shuffle_method="disk")
>       assert result.npartitions == result.optimize().npartitions

dask/dataframe/dask_expr/tests/test_shuffle.py:652: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_empty_partitions _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_empty_partitions():
        # See https://github.com/dask/dask/issues/2408
        df = pd.DataFrame({"a": list(range(10))})
>       df["b"] = df["a"] % 3

dask/dataframe/dask_expr/tests/test_shuffle.py:715: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a
0  0
1  1
2  2
3  3
4  4
5  5
6  6
7  7
8  8
9  9, key = 'b'
value = 0    0
1    1
2    2
3    0
4    1
5    2
6    0
7    1
8    2
9    0
Name: a, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_set_index_user_divisions_one_partition __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_user_divisions_one_partition(pdf):
        df = from_pandas(pdf, npartitions=1)
        result = df.set_index("x", divisions=[0, 10, 20, 21])
>       assert_eq(result, pdf.set_index("x"))

dask/dataframe/dask_expr/tests/test_shuffle.py:798: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_set_index_divisions_npartitions _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_divisions_npartitions(pdf):
        df = from_pandas(pdf, npartitions=1, sort=False)
        result = df.set_index("x", sort=True, npartitions=2)
        assert result.known_divisions
>       assert result.optimize().known_divisions

dask/dataframe/dask_expr/tests/test_shuffle.py:805: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_sort_values_unordered_categorical ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_sort_values_unordered_categorical():
        df = pd.DataFrame()
        df["a"] = np.arange(10)[::-1]
>       df["b"] = df["a"].astype("category")

dask/dataframe/dask_expr/tests/test_shuffle.py:812: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a
0  9
1  8
2  7
3  6
4  5
5  4
6  3
7  2
8  1
9  0, key = 'b'
value = 0    9
1    8
2    7
3    6
4    5
5    4
6    3
7    2
8    1
9    0
Name: a, dtype: category
Categories (10, int64): [0, 1, 2, 3, ..., 6, 7, 8, 9]

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_set_index_before_assign _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                    x      y
npartitions=10              
0               int64  int64
10   ...       ...    ...
90                ...    ...
99                ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_before_assign(df, pdf):
        result = df.set_index("x")
>       result["z"] = result.y + 1

dask/dataframe/dask_expr/tests/test_shuffle.py:821: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = 'z'
value = Series([], Name: y, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_set_index_shuffle_afterwards _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

pdf =      x   y
0    0   0
1    1   1
2    2   2
3    3   3
4    4   4
..  ..  ..
95  15  95
96  16  96
97  17  97
98  18  98
99  19  99

[100 rows x 2 columns]

    def test_set_index_shuffle_afterwards(pdf):
        ddf = from_pandas(pdf, npartitions=1)
        ddf = ddf.set_index("y", sort=True, divisions=[0, 10, 20, 100], shuffle="tasks")
        result = ddf.reset_index().y.unique()
        expected = pd.Series(pdf.y.unique(), name="y")
>       assert_eq(result, expected, check_index=False)

dask/dataframe/dask_expr/tests/test_shuffle.py:832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:32: in assert_eq
    if hasattr(obj, "dask"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_pandas_read_text_dtype_coercion[read_csv-files0] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_csv at 0x7f5bfceff480>
files = {'2014-01-01.csv': b'name,amount,id\nAlice,100,1\nBob,200,2\nCharlie,300,3\n', '2014-01-02.csv': b'name,amount,id\n', '2014-01-03.csv': b'name,amount,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @csv_and_table
    def test_pandas_read_text_dtype_coercion(reader, files):
        b = files["2014-01-01.csv"]
>       df = pandas_read_text(reader, b, b"", {}, {"amount": "float"})

dask/dataframe/io/tests/test_csv.py:181: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df =       name  amount  id
0    Alice     100   1
1      Bob     200   2
2  Charlie     300   3
dtypes = {'amount': 'float'}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+-------+----------+
E           | Column | Found | Expected |
E           +--------+-------+----------+
E           | amount | int64 | float    |
E           +--------+-------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'int64'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
___________ test_pandas_read_text_dtype_coercion[read_table-files1] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_table at 0x7f5bfceffb60>
files = {'2014-01-01.csv': b'name\tamount\tid\nAlice\t100\t1\nBob\t200\t2\nCharlie\t300\t3\n', '2014-01-02.csv': b'name\tamount\tid\n', '2014-01-03.csv': b'name\tamount\tid\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @csv_and_table
    def test_pandas_read_text_dtype_coercion(reader, files):
        b = files["2014-01-01.csv"]
>       df = pandas_read_text(reader, b, b"", {}, {"amount": "float"})

dask/dataframe/io/tests/test_csv.py:181: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df =       name  amount  id
0    Alice     100   1
1      Bob     200   2
2  Charlie     300   3
dtypes = {'amount': 'float'}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+-------+----------+
E           | Column | Found | Expected |
E           +--------+-------+----------+
E           | amount | int64 | float    |
E           +--------+-------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'int64'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
____________ test_pandas_read_text_dtype_coercion[read_fwf-files2] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_fwf at 0x7f5bfcf041a0>
files = {'2014-01-01.csv': b'    name  amount  id\n   Alice     100   1\n     Bob     200   2\n Charlie     300   3\n', '2014-...  id\n', '2014-01-03.csv': b'    name  amount  id\n  Dennis     400   4\n   Edith     500   5\n   Frank     600   6\n'}

    @csv_and_table
    def test_pandas_read_text_dtype_coercion(reader, files):
        b = files["2014-01-01.csv"]
>       df = pandas_read_text(reader, b, b"", {}, {"amount": "float"})

dask/dataframe/io/tests/test_csv.py:181: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df =       name  amount  id
0    Alice     100   1
1      Bob     200   2
2  Charlie     300   3
dtypes = {'amount': 'float'}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+-------+----------+
E           | Column | Found | Expected |
E           +--------+-------+----------+
E           | amount | int64 | float    |
E           +--------+-------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'int64'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________ test_text_blocks_to_pandas_simple[read_csv-files0] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_csv at 0x7f5bfceff480>
files = {'2014-01-01.csv': b'name,amount,id\nAlice,100,1\nBob,200,2\nCharlie,300,3\n', '2014-01-02.csv': b'name,amount,id\n', '2014-01-03.csv': b'name,amount,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @csv_and_table
    def test_text_blocks_to_pandas_simple(reader, files):
        blocks = [[files[k]] for k in sorted(files)]
        kwargs = {}
        head = pandas_read_text(reader, files["2014-01-01.csv"], b"", {})
        header = files["2014-01-01.csv"].split(b"\n")[0] + b"\n"
    
        df = text_blocks_to_pandas(reader, blocks, header, head, kwargs)
        assert isinstance(df, dd.DataFrame)
        assert list(df.columns) == ["name", "amount", "id"]
    
        values = text_blocks_to_pandas(reader, blocks, header, head, kwargs)
        assert isinstance(values, dd.DataFrame)
        assert hasattr(values, "dask")
        assert len(values.dask) == 6 if pyarrow_strings_enabled() else 3
    
>       assert_eq(df.amount.sum(), 100 + 200 + 300 + 400 + 500 + 600)

dask/dataframe/io/tests/test_csv.py:212: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [amount]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________ test_text_blocks_to_pandas_simple[read_table-files1] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_table at 0x7f5bfceffb60>
files = {'2014-01-01.csv': b'name\tamount\tid\nAlice\t100\t1\nBob\t200\t2\nCharlie\t300\t3\n', '2014-01-02.csv': b'name\tamount\tid\n', '2014-01-03.csv': b'name\tamount\tid\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @csv_and_table
    def test_text_blocks_to_pandas_simple(reader, files):
        blocks = [[files[k]] for k in sorted(files)]
        kwargs = {}
        head = pandas_read_text(reader, files["2014-01-01.csv"], b"", {})
        header = files["2014-01-01.csv"].split(b"\n")[0] + b"\n"
    
        df = text_blocks_to_pandas(reader, blocks, header, head, kwargs)
        assert isinstance(df, dd.DataFrame)
        assert list(df.columns) == ["name", "amount", "id"]
    
        values = text_blocks_to_pandas(reader, blocks, header, head, kwargs)
        assert isinstance(values, dd.DataFrame)
        assert hasattr(values, "dask")
        assert len(values.dask) == 6 if pyarrow_strings_enabled() else 3
    
>       assert_eq(df.amount.sum(), 100 + 200 + 300 + 400 + 500 + 600)

dask/dataframe/io/tests/test_csv.py:212: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [amount]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________ test_text_blocks_to_pandas_simple[read_fwf-files2] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_fwf at 0x7f5bfcf041a0>
files = {'2014-01-01.csv': b'    name  amount  id\n   Alice     100   1\n     Bob     200   2\n Charlie     300   3\n', '2014-...  id\n', '2014-01-03.csv': b'    name  amount  id\n  Dennis     400   4\n   Edith     500   5\n   Frank     600   6\n'}

    @csv_and_table
    def test_text_blocks_to_pandas_simple(reader, files):
        blocks = [[files[k]] for k in sorted(files)]
        kwargs = {}
        head = pandas_read_text(reader, files["2014-01-01.csv"], b"", {})
        header = files["2014-01-01.csv"].split(b"\n")[0] + b"\n"
    
        df = text_blocks_to_pandas(reader, blocks, header, head, kwargs)
        assert isinstance(df, dd.DataFrame)
        assert list(df.columns) == ["name", "amount", "id"]
    
        values = text_blocks_to_pandas(reader, blocks, header, head, kwargs)
        assert isinstance(values, dd.DataFrame)
        assert hasattr(values, "dask")
        assert len(values.dask) == 6 if pyarrow_strings_enabled() else 3
    
>       assert_eq(df.amount.sum(), 100 + 200 + 300 + 400 + 500 + 600)

dask/dataframe/io/tests/test_csv.py:212: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [amount]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________ test_text_blocks_to_pandas_kwargs[read_csv-files0] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_csv at 0x7f5bfceff480>
files = {'2014-01-01.csv': b'name,amount,id\nAlice,100,1\nBob,200,2\nCharlie,300,3\n', '2014-01-02.csv': b'name,amount,id\n', '2014-01-03.csv': b'name,amount,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @csv_and_table
    def test_text_blocks_to_pandas_kwargs(reader, files):
        blocks = [files[k] for k in sorted(files)]
        blocks = [[b] for b in blocks]
        kwargs = {"usecols": ["name", "id"]}
        head = pandas_read_text(reader, files["2014-01-01.csv"], b"", kwargs)
        header = files["2014-01-01.csv"].split(b"\n")[0] + b"\n"
    
        df = text_blocks_to_pandas(reader, blocks, header, head, kwargs)
        assert list(df.columns) == ["name", "id"]
>       result = df.compute()

dask/dataframe/io/tests/test_csv.py:225: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, id]
Index: []
dtypes = {'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________ test_text_blocks_to_pandas_kwargs[read_table-files1] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_table at 0x7f5bfceffb60>
files = {'2014-01-01.csv': b'name\tamount\tid\nAlice\t100\t1\nBob\t200\t2\nCharlie\t300\t3\n', '2014-01-02.csv': b'name\tamount\tid\n', '2014-01-03.csv': b'name\tamount\tid\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @csv_and_table
    def test_text_blocks_to_pandas_kwargs(reader, files):
        blocks = [files[k] for k in sorted(files)]
        blocks = [[b] for b in blocks]
        kwargs = {"usecols": ["name", "id"]}
        head = pandas_read_text(reader, files["2014-01-01.csv"], b"", kwargs)
        header = files["2014-01-01.csv"].split(b"\n")[0] + b"\n"
    
        df = text_blocks_to_pandas(reader, blocks, header, head, kwargs)
        assert list(df.columns) == ["name", "id"]
>       result = df.compute()

dask/dataframe/io/tests/test_csv.py:225: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, id]
Index: []
dtypes = {'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________ test_text_blocks_to_pandas_kwargs[read_fwf-files2] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_fwf at 0x7f5bfcf041a0>
files = {'2014-01-01.csv': b'    name  amount  id\n   Alice     100   1\n     Bob     200   2\n Charlie     300   3\n', '2014-...  id\n', '2014-01-03.csv': b'    name  amount  id\n  Dennis     400   4\n   Edith     500   5\n   Frank     600   6\n'}

    @csv_and_table
    def test_text_blocks_to_pandas_kwargs(reader, files):
        blocks = [files[k] for k in sorted(files)]
        blocks = [[b] for b in blocks]
        kwargs = {"usecols": ["name", "id"]}
        head = pandas_read_text(reader, files["2014-01-01.csv"], b"", kwargs)
        header = files["2014-01-01.csv"].split(b"\n")[0] + b"\n"
    
        df = text_blocks_to_pandas(reader, blocks, header, head, kwargs)
        assert list(df.columns) == ["name", "id"]
>       result = df.compute()

dask/dataframe/io/tests/test_csv.py:225: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, id]
Index: []
dtypes = {'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________ test_text_blocks_to_pandas_blocked[read_csv-files0] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_csv at 0x7f5bfceff480>
files = {'2014-01-01.csv': b'name,amount,id\nAlice,100,1\nBob,200,2\nCharlie,300,3\n', '2014-01-02.csv': b'name,amount,id\n', '2014-01-03.csv': b'name,amount,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @csv_and_table
    def test_text_blocks_to_pandas_blocked(reader, files):
        expected = read_files()
        header = files["2014-01-01.csv"].split(b"\n")[0] + b"\n"
        blocks = []
        for k in sorted(files):
            b = files[k]
            lines = b.split(b"\n")
            blocks.append([b"\n".join(bs) for bs in partition_all(2, lines)])
    
        df = text_blocks_to_pandas(reader, blocks, header, expected.head(), {})
        assert_eq(
>           df.compute().reset_index(drop=True),
            expected.reset_index(drop=True),
            check_dtype=False,
        )

dask/dataframe/io/tests/test_csv.py:241: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': string[pyarrow]}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | name   | object | string   |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - name
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'name': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
____________ test_text_blocks_to_pandas_blocked[read_table-files1] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_table at 0x7f5bfceffb60>
files = {'2014-01-01.csv': b'name\tamount\tid\nAlice\t100\t1\nBob\t200\t2\nCharlie\t300\t3\n', '2014-01-02.csv': b'name\tamount\tid\n', '2014-01-03.csv': b'name\tamount\tid\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @csv_and_table
    def test_text_blocks_to_pandas_blocked(reader, files):
        expected = read_files()
        header = files["2014-01-01.csv"].split(b"\n")[0] + b"\n"
        blocks = []
        for k in sorted(files):
            b = files[k]
            lines = b.split(b"\n")
            blocks.append([b"\n".join(bs) for bs in partition_all(2, lines)])
    
        df = text_blocks_to_pandas(reader, blocks, header, expected.head(), {})
        assert_eq(
>           df.compute().reset_index(drop=True),
            expected.reset_index(drop=True),
            check_dtype=False,
        )

dask/dataframe/io/tests/test_csv.py:241: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': string[pyarrow]}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | name   | object | string   |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - name
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'name': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________ test_text_blocks_to_pandas_blocked[read_fwf-files2] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_fwf at 0x7f5bfcf041a0>
files = {'2014-01-01.csv': b'    name  amount  id\n   Alice     100   1\n     Bob     200   2\n Charlie     300   3\n', '2014-...  id\n', '2014-01-03.csv': b'    name  amount  id\n  Dennis     400   4\n   Edith     500   5\n   Frank     600   6\n'}

    @csv_and_table
    def test_text_blocks_to_pandas_blocked(reader, files):
        expected = read_files()
        header = files["2014-01-01.csv"].split(b"\n")[0] + b"\n"
        blocks = []
        for k in sorted(files):
            b = files[k]
            lines = b.split(b"\n")
            blocks.append([b"\n".join(bs) for bs in partition_all(2, lines)])
    
        df = text_blocks_to_pandas(reader, blocks, header, expected.head(), {})
        assert_eq(
>           df.compute().reset_index(drop=True),
            expected.reset_index(drop=True),
            check_dtype=False,
        )

dask/dataframe/io/tests/test_csv.py:241: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': string[pyarrow]}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | name   | object | string   |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - name
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'name': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
___________________ test_skiprows[read_csv-read_csv-files0] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99dc70>
pd_read = <function read_csv at 0x7f5bfceff480>
files = {'2014-01-01.csv': b'# some header lines\n# that may be present\n# in a data file\n# before any data\nname,amount,id\n...n# that may be present\n# in a data file\n# before any data\nname,amount,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @pytest.mark.parametrize(
        "dd_read,pd_read,files",
        [(dd.read_csv, pd.read_csv, csv_files), (dd.read_table, pd.read_table, tsv_files)],
    )
    def test_skiprows(dd_read, pd_read, files):
        files = {name: comment_header + b"\n" + content for name, content in files.items()}
        skip = len(comment_header.splitlines())
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", skiprows=skip)
            expected_df = read_files_with(files, pd_read, skiprows=skip)
>           assert_eq(df, expected_df, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:267: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_________________ test_skiprows[read_table-read_table-files1] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99db10>
pd_read = <function read_table at 0x7f5bfceffb60>
files = {'2014-01-01.csv': b'# some header lines\n# that may be present\n# in a data file\n# before any data\nname\tamount\tid...may be present\n# in a data file\n# before any data\nname\tamount\tid\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @pytest.mark.parametrize(
        "dd_read,pd_read,files",
        [(dd.read_csv, pd.read_csv, csv_files), (dd.read_table, pd.read_table, tsv_files)],
    )
    def test_skiprows(dd_read, pd_read, files):
        files = {name: comment_header + b"\n" + content for name, content in files.items()}
        skip = len(comment_header.splitlines())
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", skiprows=skip)
            expected_df = read_files_with(files, pd_read, skiprows=skip)
>           assert_eq(df, expected_df, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:267: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
____________________ test_comment[read_csv-read_csv-files0] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99dc70>
pd_read = <function read_csv at 0x7f5bfceff480>
files = {'2014-01-01.csv': b'# some header lines\n# that may be present\n# in a data file\n# before any data\nname,amount,id# ...ent\n# in a data file\n# before any data\nname,amount,id# just some comment\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @pytest.mark.parametrize(
        "dd_read,pd_read,files",
        [(dd.read_csv, pd.read_csv, csv_files), (dd.read_table, pd.read_table, tsv_files)],
    )
    def test_comment(dd_read, pd_read, files):
        files = {
            name: comment_header
            + b"\n"
            + content.replace(b"\n", b"# just some comment\n", 1)
            for name, content in files.items()
        }
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", comment="#")
            expected_df = read_files_with(files, pd_read, comment="#")
>           assert_eq(df, expected_df, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:284: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
__________________ test_comment[read_table-read_table-files1] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99db10>
pd_read = <function read_table at 0x7f5bfceffb60>
files = {'2014-01-01.csv': b'# some header lines\n# that may be present\n# in a data file\n# before any data\nname\tamount\tid...n a data file\n# before any data\nname\tamount\tid# just some comment\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @pytest.mark.parametrize(
        "dd_read,pd_read,files",
        [(dd.read_csv, pd.read_csv, csv_files), (dd.read_table, pd.read_table, tsv_files)],
    )
    def test_comment(dd_read, pd_read, files):
        files = {
            name: comment_header
            + b"\n"
            + content.replace(b"\n", b"# just some comment\n", 1)
            for name, content in files.items()
        }
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", comment="#")
            expected_df = read_files_with(files, pd_read, comment="#")
>           assert_eq(df, expected_df, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:284: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
__________________ test_skipfooter[read_csv-read_csv-files0] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99dc70>
pd_read = <function read_csv at 0x7f5bfceff480>
files = {'2014-01-01.csv': b'name,amount,id\nAlice,100,1\nBob,200,2\nCharlie,300,3\n\n# some footer lines\n# that may be prese...nt,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n\n# some footer lines\n# that may be present\n# at the end of the file'}

    @pytest.mark.parametrize(
        "dd_read,pd_read,files",
        [(dd.read_csv, pd.read_csv, csv_files), (dd.read_table, pd.read_table, tsv_files)],
    )
    def test_skipfooter(dd_read, pd_read, files):
        files = {name: content + b"\n" + comment_footer for name, content in files.items()}
        skip = len(comment_footer.splitlines())
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", skipfooter=skip, engine="python")
            expected_df = read_files_with(files, pd_read, skipfooter=skip, engine="python")
>           assert_eq(df, expected_df, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:297: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df =      name  amount  id
0  Dennis     400   4
1   Edith     500   5
2   Frank     600   6
dtypes = {'amount': dtype('float64'), 'id': dtype('float64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+-------+----------+
E           | Column | Found | Expected |
E           +--------+-------+----------+
E           | amount | int64 | float64  |
E           | id     | int64 | float64  |
E           +--------+-------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'int64',
E                  'id': 'int64'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
________________ test_skipfooter[read_table-read_table-files1] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99db10>
pd_read = <function read_table at 0x7f5bfceffb60>
files = {'2014-01-01.csv': b'name\tamount\tid\nAlice\t100\t1\nBob\t200\t2\nCharlie\t300\t3\n\n# some footer lines\n# that may ...nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n\n# some footer lines\n# that may be present\n# at the end of the file'}

    @pytest.mark.parametrize(
        "dd_read,pd_read,files",
        [(dd.read_csv, pd.read_csv, csv_files), (dd.read_table, pd.read_table, tsv_files)],
    )
    def test_skipfooter(dd_read, pd_read, files):
        files = {name: content + b"\n" + comment_footer for name, content in files.items()}
        skip = len(comment_footer.splitlines())
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", skipfooter=skip, engine="python")
            expected_df = read_files_with(files, pd_read, skipfooter=skip, engine="python")
>           assert_eq(df, expected_df, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:297: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df =      name  amount  id
0  Dennis     400   4
1   Edith     500   5
2   Frank     600   6
dtypes = {'amount': dtype('float64'), 'id': dtype('float64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+-------+----------+
E           | Column | Found | Expected |
E           +--------+-------+----------+
E           | amount | int64 | float64  |
E           | id     | int64 | float64  |
E           +--------+-------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'int64',
E                  'id': 'int64'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_______ test_skiprows_as_list[read_csv-read_csv-files0-str, int, int\n] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99dc70>
pd_read = <function read_csv at 0x7f5bfceff480>
files = {'2014-01-01.csv': b'# some header lines\n# that may be present\n# in a data file\n# before any data\nname,amount,id\n...present\n# in a data file\n# before any data\nname,amount,id\nstr, int, int\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}
units = b'str, int, int\n'

    @pytest.mark.parametrize(
        "dd_read,pd_read,files,units",
        [
            (dd.read_csv, pd.read_csv, csv_files, csv_units_row),
            (dd.read_table, pd.read_table, tsv_files, tsv_units_row),
        ],
    )
    def test_skiprows_as_list(dd_read, pd_read, files, units):
        files = {
            name: (comment_header + b"\n" + content.replace(b"\n", b"\n" + units, 1))
            for name, content in files.items()
        }
        skip = [0, 1, 2, 3, 5]
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", skiprows=skip)
            expected_df = read_files_with(files, pd_read, skiprows=skip)
>           assert_eq(df, expected_df, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:316: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
____ test_skiprows_as_list[read_table-read_table-files1-str\t int\t int\n] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99db10>
pd_read = <function read_table at 0x7f5bfceffb60>
files = {'2014-01-01.csv': b'# some header lines\n# that may be present\n# in a data file\n# before any data\nname\tamount\tid... in a data file\n# before any data\nname\tamount\tid\nstr\t int\t int\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}
units = b'str\t int\t int\n'

    @pytest.mark.parametrize(
        "dd_read,pd_read,files,units",
        [
            (dd.read_csv, pd.read_csv, csv_files, csv_units_row),
            (dd.read_table, pd.read_table, tsv_files, tsv_units_row),
        ],
    )
    def test_skiprows_as_list(dd_read, pd_read, files, units):
        files = {
            name: (comment_header + b"\n" + content.replace(b"\n", b"\n" + units, 1))
            for name, content in files.items()
        }
        skip = [0, 1, 2, 3, 5]
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", skiprows=skip)
            expected_df = read_files_with(files, pd_read, skiprows=skip)
>           assert_eq(df, expected_df, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:316: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
____________________ test_enforce_dtypes[read_csv-blocks0] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_csv at 0x7f5bfceff480>
blocks = [[b'aa,bb\n1,1.0\n2,2.0', b'10,20\n30,40'], [b'aa,bb\n1,1.0\n2,2.0', b'10,20\n30,40']]

    @pytest.mark.parametrize(
        "reader,blocks", [(pd.read_csv, csv_blocks), (pd.read_table, tsv_blocks)]
    )
    def test_enforce_dtypes(reader, blocks):
        head = reader(BytesIO(blocks[0][0]), header=0)
        header = blocks[0][0].split(b"\n")[0] + b"\n"
        dfs = text_blocks_to_pandas(reader, blocks, header, head, {})
>       dfs = dask.compute(dfs, scheduler="sync")

dask/dataframe/io/tests/test_csv.py:337: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df =    aa  bb
0  10  20
1  30  40
dtypes = {'aa': dtype('int64'), 'bb': dtype('float64')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+-------+----------+
E           | Column | Found | Expected |
E           +--------+-------+----------+
E           | bb     | int64 | float64  |
E           +--------+-------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - bb
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'bb': 'int64'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
___________________ test_enforce_dtypes[read_table-blocks1] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_table at 0x7f5bfceffb60>
blocks = [[b'aa\tbb\n1\t1.0\n2\t2.0', b'10\t20\n30\t40'], [b'aa\tbb\n1\t1.0\n2\t2.0', b'10\t20\n30\t40']]

    @pytest.mark.parametrize(
        "reader,blocks", [(pd.read_csv, csv_blocks), (pd.read_table, tsv_blocks)]
    )
    def test_enforce_dtypes(reader, blocks):
        head = reader(BytesIO(blocks[0][0]), header=0)
        header = blocks[0][0].split(b"\n")[0] + b"\n"
        dfs = text_blocks_to_pandas(reader, blocks, header, head, {})
>       dfs = dask.compute(dfs, scheduler="sync")

dask/dataframe/io/tests/test_csv.py:337: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df =    aa  bb
0  10  20
1  30  40
dtypes = {'aa': dtype('int64'), 'bb': dtype('float64')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+-------+----------+
E           | Column | Found | Expected |
E           +--------+-------+----------+
E           | bb     | int64 | float64  |
E           +--------+-------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - bb
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'bb': 'int64'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
____________________ test_enforce_columns[read_csv-blocks0] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_csv at 0x7f5bfceff480>
blocks = [[b'aa,bb\n1,1.0\n2,2.0', b'10,20\n30,40'], [b'AA,bb\n1,1.0\n2,2.0', b'10,20\n30,40']]

    @pytest.mark.parametrize(
        "reader,blocks", [(pd.read_csv, csv_blocks), (pd.read_table, tsv_blocks)]
    )
    def test_enforce_columns(reader, blocks):
        # Replace second header with different column name
        blocks = [blocks[0], [blocks[1][0].replace(b"a", b"A"), blocks[1][1]]]
        head = reader(BytesIO(blocks[0][0]), header=0)
        header = blocks[0][0].split(b"\n")[0] + b"\n"
>       with pytest.raises(ValueError):
E       Failed: DID NOT RAISE <class 'ValueError'>

dask/dataframe/io/tests/test_csv.py:349: Failed
___________________ test_enforce_columns[read_table-blocks1] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

reader = <function read_table at 0x7f5bfceffb60>
blocks = [[b'aa\tbb\n1\t1.0\n2\t2.0', b'10\t20\n30\t40'], [b'AA\tbb\n1\t1.0\n2\t2.0', b'10\t20\n30\t40']]

    @pytest.mark.parametrize(
        "reader,blocks", [(pd.read_csv, csv_blocks), (pd.read_table, tsv_blocks)]
    )
    def test_enforce_columns(reader, blocks):
        # Replace second header with different column name
        blocks = [blocks[0], [blocks[1][0].replace(b"a", b"A"), blocks[1][1]]]
        head = reader(BytesIO(blocks[0][0]), header=0)
        header = blocks[0][0].split(b"\n")[0] + b"\n"
>       with pytest.raises(ValueError):
E       Failed: DID NOT RAISE <class 'ValueError'>

dask/dataframe/io/tests/test_csv.py:349: Failed
________________ test_read_csv_files[read_csv-read_csv-files0] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99dc70>
pd_read = <function read_csv at 0x7f5bfceff480>
files = {'2014-01-01.csv': b'name,amount,id\nAlice,100,1\nBob,200,2\nCharlie,300,3\n', '2014-01-02.csv': b'name,amount,id\n', '2014-01-03.csv': b'name,amount,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @pytest.mark.parametrize(
        "dd_read,pd_read,files",
        [(dd.read_csv, pd.read_csv, csv_files), (dd.read_table, pd.read_table, tsv_files)],
    )
    def test_read_csv_files(dd_read, pd_read, files):
        expected = read_files()
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv")
>           assert_eq(df, expected, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:428: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________ test_read_csv_files[read_table-read_table-files1] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99db10>
pd_read = <function read_table at 0x7f5bfceffb60>
files = {'2014-01-01.csv': b'name\tamount\tid\nAlice\t100\t1\nBob\t200\t2\nCharlie\t300\t3\n', '2014-01-02.csv': b'name\tamount\tid\n', '2014-01-03.csv': b'name\tamount\tid\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @pytest.mark.parametrize(
        "dd_read,pd_read,files",
        [(dd.read_csv, pd.read_csv, csv_files), (dd.read_table, pd.read_table, tsv_files)],
    )
    def test_read_csv_files(dd_read, pd_read, files):
        expected = read_files()
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv")
>           assert_eq(df, expected, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:428: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________ test_read_csv_files_list[read_csv-read_csv-files0] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99dc70>
pd_read = <function read_csv at 0x7f5bfceff480>
files = {'2014-01-01.csv': b'name,amount,id\nAlice,100,1\nBob,200,2\nCharlie,300,3\n', '2014-01-02.csv': b'name,amount,id\n', '2014-01-03.csv': b'name,amount,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @pytest.mark.parametrize(
        "dd_read,pd_read,files",
        [(dd.read_csv, pd.read_csv, csv_files), (dd.read_table, pd.read_table, tsv_files)],
    )
    def test_read_csv_files_list(dd_read, pd_read, files):
        with filetexts(files, mode="b"):
            subset = sorted(files)[:2]  # Just first 2
            sol = read_files(subset)
            res = dd_read(subset)
>           assert_eq(res, sol, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:445: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
____________ test_read_csv_files_list[read_table-read_table-files1] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99db10>
pd_read = <function read_table at 0x7f5bfceffb60>
files = {'2014-01-01.csv': b'name\tamount\tid\nAlice\t100\t1\nBob\t200\t2\nCharlie\t300\t3\n', '2014-01-02.csv': b'name\tamount\tid\n', '2014-01-03.csv': b'name\tamount\tid\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @pytest.mark.parametrize(
        "dd_read,pd_read,files",
        [(dd.read_csv, pd.read_csv, csv_files), (dd.read_table, pd.read_table, tsv_files)],
    )
    def test_read_csv_files_list(dd_read, pd_read, files):
        with filetexts(files, mode="b"):
            subset = sorted(files)[:2]  # Just first 2
            sol = read_files(subset)
            res = dd_read(subset)
>           assert_eq(res, sol, check_dtype=False)

dask/dataframe/io/tests/test_csv.py:445: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________ test_read_csv_include_path_column[read_csv-files0] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99dc70>
files = {'2014-01-01.csv': b'name,amount,id\nAlice,100,1\nBob,200,2\nCharlie,300,3\n', '2014-01-02.csv': b'name,amount,id\n', '2014-01-03.csv': b'name,amount,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @pytest.mark.parametrize(
        "dd_read,files", [(dd.read_csv, csv_files), (dd.read_table, tsv_files)]
    )
    def test_read_csv_include_path_column(dd_read, files):
        with filetexts(files, mode="b"):
            df = dd_read(
                "2014-01-*.csv",
                include_path_column=True,
                converters={"path": parse_filename},
            )
>           filenames = df.path.compute().unique()

dask/dataframe/io/tests/test_csv.py:461: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________ test_read_csv_include_path_column[read_table-files1] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99db10>
files = {'2014-01-01.csv': b'name\tamount\tid\nAlice\t100\t1\nBob\t200\t2\nCharlie\t300\t3\n', '2014-01-02.csv': b'name\tamount\tid\n', '2014-01-03.csv': b'name\tamount\tid\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @pytest.mark.parametrize(
        "dd_read,files", [(dd.read_csv, csv_files), (dd.read_table, tsv_files)]
    )
    def test_read_csv_include_path_column(dd_read, files):
        with filetexts(files, mode="b"):
            df = dd_read(
                "2014-01-*.csv",
                include_path_column=True,
                converters={"path": parse_filename},
            )
>           filenames = df.path.compute().unique()

dask/dataframe/io/tests/test_csv.py:461: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
__________ test_read_csv_include_path_column_as_str[read_csv-files0] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99dc70>
files = {'2014-01-01.csv': b'name,amount,id\nAlice,100,1\nBob,200,2\nCharlie,300,3\n', '2014-01-02.csv': b'name,amount,id\n', '2014-01-03.csv': b'name,amount,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @pytest.mark.parametrize(
        "dd_read,files", [(dd.read_csv, csv_files), (dd.read_table, tsv_files)]
    )
    def test_read_csv_include_path_column_as_str(dd_read, files):
        with filetexts(files, mode="b"):
            df = dd_read(
                "2014-01-*.csv",
                include_path_column="filename",
                converters={"filename": parse_filename},
            )
>           filenames = df.filename.compute().unique()

dask/dataframe/io/tests/test_csv.py:477: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_________ test_read_csv_include_path_column_as_str[read_table-files1] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99db10>
files = {'2014-01-01.csv': b'name\tamount\tid\nAlice\t100\t1\nBob\t200\t2\nCharlie\t300\t3\n', '2014-01-02.csv': b'name\tamount\tid\n', '2014-01-03.csv': b'name\tamount\tid\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @pytest.mark.parametrize(
        "dd_read,files", [(dd.read_csv, csv_files), (dd.read_table, tsv_files)]
    )
    def test_read_csv_include_path_column_as_str(dd_read, files):
        with filetexts(files, mode="b"):
            df = dd_read(
                "2014-01-*.csv",
                include_path_column="filename",
                converters={"filename": parse_filename},
            )
>           filenames = df.filename.compute().unique()

dask/dataframe/io/tests/test_csv.py:477: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____ test_read_csv_include_path_column_is_dtype_category[read_csv-files0] _____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99dc70>
files = {'2014-01-01.csv': b'name,amount,id\nAlice,100,1\nBob,200,2\nCharlie,300,3\n', '2014-01-02.csv': b'name,amount,id\n', '2014-01-03.csv': b'name,amount,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @pytest.mark.parametrize(
        "dd_read,files", [(dd.read_csv, csv_files), (dd.read_table, tsv_files)]
    )
    def test_read_csv_include_path_column_is_dtype_category(dd_read, files):
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", include_path_column=True)
            assert df.path.dtype == "category"
            assert has_known_categories(df.path)
    
            dfs = dd_read("2014-01-*.csv", include_path_column=True)
>           result = dfs.compute()

dask/dataframe/io/tests/test_csv.py:502: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
____ test_read_csv_include_path_column_is_dtype_category[read_table-files1] ____
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99db10>
files = {'2014-01-01.csv': b'name\tamount\tid\nAlice\t100\t1\nBob\t200\t2\nCharlie\t300\t3\n', '2014-01-02.csv': b'name\tamount\tid\n', '2014-01-03.csv': b'name\tamount\tid\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @pytest.mark.parametrize(
        "dd_read,files", [(dd.read_csv, csv_files), (dd.read_table, tsv_files)]
    )
    def test_read_csv_include_path_column_is_dtype_category(dd_read, files):
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", include_path_column=True)
            assert df.path.dtype == "category"
            assert has_known_categories(df.path)
    
            dfs = dd_read("2014-01-*.csv", include_path_column=True)
>           result = dfs.compute()

dask/dataframe/io/tests/test_csv.py:502: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_ test_read_csv_include_path_column_with_multiple_partitions_per_file[read_csv-files0] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99dc70>
files = {'2014-01-01.csv': b'name,amount,id\nAlice,100,1\nBob,200,2\nCharlie,300,3\n', '2014-01-02.csv': b'name,amount,id\n', '2014-01-03.csv': b'name,amount,id\nDennis,400,4\nEdith,500,5\nFrank,600,6\n'}

    @pytest.mark.parametrize(
        "dd_read,files", [(dd.read_csv, csv_files), (dd.read_table, tsv_files)]
    )
    def test_read_csv_include_path_column_with_multiple_partitions_per_file(dd_read, files):
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", blocksize="10B", include_path_column=True)
            assert df.npartitions > 3
            assert df.path.dtype == "category"
            assert has_known_categories(df.path)
    
            dfs = dd_read("2014-01-*.csv", blocksize="10B", include_path_column=True)
>           result = dfs.compute()

dask/dataframe/io/tests/test_csv.py:518: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_ test_read_csv_include_path_column_with_multiple_partitions_per_file[read_table-files1] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dd_read = <function make_reader.<locals>.read at 0x7f5bfa99db10>
files = {'2014-01-01.csv': b'name\tamount\tid\nAlice\t100\t1\nBob\t200\t2\nCharlie\t300\t3\n', '2014-01-02.csv': b'name\tamount\tid\n', '2014-01-03.csv': b'name\tamount\tid\nDennis\t400\t4\nEdith\t500\t5\nFrank\t600\t6\n'}

    @pytest.mark.parametrize(
        "dd_read,files", [(dd.read_csv, csv_files), (dd.read_table, tsv_files)]
    )
    def test_read_csv_include_path_column_with_multiple_partitions_per_file(dd_read, files):
        with filetexts(files, mode="b"):
            df = dd_read("2014-01-*.csv", blocksize="10B", include_path_column=True)
            assert df.npartitions > 3
            assert df.path.dtype == "category"
            assert has_known_categories(df.path)
    
            dfs = dd_read("2014-01-*.csv", blocksize="10B", include_path_column=True)
>           result = dfs.compute()

dask/dataframe/io/tests/test_csv.py:518: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________________________ test_read_csv_index ______________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_read_csv_index():
        with filetext(csv_text) as fn:
            f = dd.read_csv(fn, blocksize=20).set_index("amount")
>           result = f.compute(scheduler="sync")

dask/dataframe/io/tests/test_csv.py:530: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [name, amount]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________________ test_usecols _________________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_usecols():
        with filetext(timeseries) as fn:
            df = dd.read_csv(fn, blocksize=30, usecols=["High", "Low"])
            df_select = df[["High"]]
            expected = pd.read_csv(fn, usecols=["High", "Low"])
            expected_select = expected[["High"]]
>           assert (df.compute().values == expected.values).all()

dask/dataframe/io/tests/test_csv.py:560: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [High, Low]
Index: []
dtypes = {'High': dtype('float64'), 'Low': dtype('float64')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | High   | object | float64  |
E           | Low    | object | float64  |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - High
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - Low
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'High': 'object',
E                  'Low': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
____________________________ test_consistent_dtypes ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_consistent_dtypes():
        text = normalize_text(
            """
        name,amount
        Alice,100.5
        Bob,-200.5
        Charlie,300
        Dennis,400
        Edith,-500
        Frank,600
        """
        )
    
        with filetext(text) as fn:
            df = dd.read_csv(fn, blocksize=30)
>           assert df.amount.compute().dtype == float

dask/dataframe/io/tests/test_csv.py:609: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df =      name  amount
0  Dennis     400
1   Edith    -500
2   Frank     600
dtypes = {'amount': dtype('float64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+-------+----------+
E           | Column | Found | Expected |
E           +--------+-------+----------+
E           | amount | int64 | float64  |
E           +--------+-------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'int64'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
___________________________ test_categorical_dtypes ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

args = ('foo.*.csv',)
kwargs = {'blocksize': 25, 'dtype': {'fruit': 'category'}}
func = <function make_reader.<locals>.read at 0x7f5bfa99dbc0>
exc = FutureWarning('An error occurred while calling the read_csv method registered to the pandas backend.\nOriginal Message...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
>           return func(*args, **kwargs)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:140: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:731: in read
    return read_pandas(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:628: in read_pandas
    return text_blocks_to_pandas(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:266: in text_blocks_to_pandas
    head = clear_known_categories(head, cols=unknown_categoricals)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:250: in clear_known_categories
    x[c] = x[c].cat.set_categories([UNKNOWN_CATEGORIES])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     fruit  count
0   apple     10
1   apple     25
2    pear    100
3  orange     15
key = 'fruit'
value = 0    NaN
1    NaN
2    NaN
3    NaN
Name: fruit, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning

The above exception was the direct cause of the following exception:

    def test_categorical_dtypes():
        text1 = normalize_text(
            """
        fruit,count
        apple,10
        apple,25
        pear,100
        orange,15
        """
        )
    
        text2 = normalize_text(
            """
        fruit,count
        apple,200
        banana,300
        orange,400
        banana,10
        """
        )
    
        with filetexts({"foo.1.csv": text1, "foo.2.csv": text2}):
>           df = dd.read_csv("foo.*.csv", dtype={"fruit": "category"}, blocksize=25)

dask/dataframe/io/tests/test_csv.py:659: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = ('foo.*.csv',)
kwargs = {'blocksize': 25, 'dtype': {'fruit': 'category'}}
func = <function make_reader.<locals>.read at 0x7f5bfa99dbc0>
exc = FutureWarning('An error occurred while calling the read_csv method registered to the pandas backend.\nOriginal Message...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
            return func(*args, **kwargs)
        except Exception as e:
            try:
                exc = type(e)(
                    f"An error occurred while calling the {funcname(func)} "
                    f"method registered to the {self.backend} backend.\n"
                    f"Original Message: {e}"
                )
            except TypeError:
                raise e
            else:
>               raise exc from e
E               FutureWarning: An error occurred while calling the read_csv method registered to the pandas backend.
E               Original Message: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:151: FutureWarning
____________________________ test_categorical_known ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

args = ('foo.*.csv',), kwargs = {'dtype': {'A': 'category', 'B': 'category'}}
func = <function make_reader.<locals>.read at 0x7f5bfa99dbc0>
exc = FutureWarning('An error occurred while calling the read_csv method registered to the pandas backend.\nOriginal Message...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
>           return func(*args, **kwargs)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:140: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:731: in read
    return read_pandas(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:628: in read_pandas
    return text_blocks_to_pandas(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:266: in text_blocks_to_pandas
    head = clear_known_categories(head, cols=unknown_categoricals)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:250: in clear_known_categories
    x[c] = x[c].cat.set_categories([UNKNOWN_CATEGORIES])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    A  B
0  a  a
1  b  b
2  a  a, key = 'A'
value = 0    NaN
1    NaN
2    NaN
Name: A, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning

The above exception was the direct cause of the following exception:

    def test_categorical_known():
        text1 = normalize_text(
            """
        A,B
        a,a
        b,b
        a,a
        """
        )
        text2 = normalize_text(
            """
        A,B
        a,a
        b,b
        c,c
        """
        )
        dtype = pd.api.types.CategoricalDtype(["a", "b", "c"], ordered=False)
        with filetexts({"foo.1.csv": text1, "foo.2.csv": text2}):
>           result = dd.read_csv("foo.*.csv", dtype={"A": "category", "B": "category"})

dask/dataframe/io/tests/test_csv.py:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = ('foo.*.csv',), kwargs = {'dtype': {'A': 'category', 'B': 'category'}}
func = <function make_reader.<locals>.read at 0x7f5bfa99dbc0>
exc = FutureWarning('An error occurred while calling the read_csv method registered to the pandas backend.\nOriginal Message...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
            return func(*args, **kwargs)
        except Exception as e:
            try:
                exc = type(e)(
                    f"An error occurred while calling the {funcname(func)} "
                    f"method registered to the {self.backend} backend.\n"
                    f"Original Message: {e}"
                )
            except TypeError:
                raise e
            else:
>               raise exc from e
E               FutureWarning: An error occurred while calling the read_csv method registered to the pandas backend.
E               Original Message: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:151: FutureWarning
____________________ test_read_csv_compression[None0-None] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = None, blocksize = None

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________________ test_read_csv_compression[None0-10] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = None, blocksize = 10

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________________ test_read_csv_compression[zip-None] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = 'zip', blocksize = None

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________________ test_read_csv_compression[zip-10] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = 'zip', blocksize = 10

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________________ test_read_csv_compression[bz2-None] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = 'bz2', blocksize = None

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________________ test_read_csv_compression[bz2-10] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = 'bz2', blocksize = 10

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________________ test_read_csv_compression[gzip-None] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = 'gzip', blocksize = None

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________________ test_read_csv_compression[gzip-10] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = 'gzip', blocksize = 10

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________________ test_read_csv_compression[xz-None] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = 'xz', blocksize = None

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_______________________ test_read_csv_compression[xz-10] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = 'xz', blocksize = 10

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
____________________ test_read_csv_compression[None1-None] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = None, blocksize = None

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________________ test_read_csv_compression[None1-10] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

fmt = None, blocksize = 10

    @pytest.mark.parametrize("blocksize", [None, 10])
    @pytest.mark.parametrize("fmt", compression_fmts)
    def test_read_csv_compression(fmt, blocksize):
        if fmt and fmt not in compress:
            pytest.skip("compress function not provided for %s" % fmt)
    
        expected = read_files()
        suffix = {"gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz"}.get(fmt, "")
        files2 = valmap(compress[fmt], csv_files) if fmt else csv_files
        renamed_files = {k + suffix: v for k, v in files2.items()}
        with filetexts(renamed_files, mode="b"):
            # This test is using `compression="infer"` (the default) for
            # read_csv.  The paths must have the appropriate extension.
            if fmt and blocksize:
                with pytest.warns(UserWarning):
                    df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            else:
                df = dd.read_csv("2014-01-*.csv" + suffix, blocksize=blocksize)
            assert_eq(
>               df.compute(scheduler="sync").reset_index(drop=True),
                expected.reset_index(drop=True),
                check_dtype=False,
            )

dask/dataframe/io/tests/test_csv.py:787: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________________________ test_header_int[3] ______________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

header = 3

    @pytest.mark.parametrize("header", [1, 2, 3])
    def test_header_int(header):
        text = (
            "id0,name0,x0,y0\n"
            "id,name,x,y\n"
            "1034,Victor,-0.25,0.84\n"
            "998,Xavier,-0.48,-0.13\n"
            "999,Zelda,0.00,0.47\n"
            "980,Alice,0.67,-0.98\n"
            "989,Zelda,-0.04,0.03\n"
        )
        with filetexts({"test_header_int.csv": text}):
            df = dd.read_csv("test_header_int.csv", header=header, blocksize=64)
            expected = pd.read_csv("test_header_int.csv", header=header)
>           assert_eq(df, expected, check_index=False)

dask/dataframe/io/tests/test_csv.py:837: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [998, Xavier, -0.48, -0.13]
Index: []
dtypes = {'-0.13': dtype('float64'), '-0.48': dtype('float64'), '998': dtype('int64'), 'Xavier': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | -0.48  | object | float64  |
E           | 998    | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - -0.48
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - 998
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'-0.48': 'object',
E                  '998': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
_____________________________ test_assume_missing ______________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

args = ('/tmp/tmpnxfza2gu',), kwargs = {'assume_missing': True, 'sample': 50}
func = <function make_reader.<locals>.read at 0x7f5bfa99dbc0>
exc = FutureWarning('An error occurred while calling the read_csv method registered to the pandas backend.\nOriginal Message...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
>           return func(*args, **kwargs)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:140: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:731: in read
    return read_pandas(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:618: in read_pandas
    head[c] = head[c].astype(float)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    numbers names  more_numbers  integers
0        1   foo             2         3
1        1   foo             2         3
key = 'numbers', value = 0    1.0
1    1.0
Name: numbers, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning

The above exception was the direct cause of the following exception:

    def test_assume_missing():
        text = "numbers,names,more_numbers,integers\n"
        for _ in range(1000):
            text += "1,foo,2,3\n"
        text += "1.5,bar,2.5,3\n"
        with filetext(text) as fn:
            sol = pd.read_csv(fn)
    
            # assume_missing affects all columns
>           res = dd.read_csv(fn, sample=50, assume_missing=True)

dask/dataframe/io/tests/test_csv.py:1091: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = ('/tmp/tmpnxfza2gu',), kwargs = {'assume_missing': True, 'sample': 50}
func = <function make_reader.<locals>.read at 0x7f5bfa99dbc0>
exc = FutureWarning('An error occurred while calling the read_csv method registered to the pandas backend.\nOriginal Message...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
            return func(*args, **kwargs)
        except Exception as e:
            try:
                exc = type(e)(
                    f"An error occurred while calling the {funcname(func)} "
                    f"method registered to the {self.backend} backend.\n"
                    f"Original Message: {e}"
                )
            except TypeError:
                raise e
            else:
>               raise exc from e
E               FutureWarning: An error occurred while calling the read_csv method registered to the pandas backend.
E               Original Message: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:151: FutureWarning
_________________________ test_robust_column_mismatch __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_robust_column_mismatch():
        files = csv_files.copy()
        k = sorted(files)[-1]
        files[k] = files[k].replace(b"name", b"Name")
        with filetexts(files, mode="b"):
            ddf = dd.read_csv(
                "2014-01-*.csv", header=None, skiprows=1, names=["name", "amount", "id"]
            )
            df = pd.read_csv("2014-01-01.csv")
            assert (df.columns == ddf.columns).all()
>           assert_eq(ddf, ddf)

dask/dataframe/io/tests/test_csv.py:1291: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
______________________ test_different_columns_are_allowed ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_different_columns_are_allowed():
        files = csv_files.copy()
        k = sorted(files)[-1]
        files[k] = files[k].replace(b"name", b"address")
        with filetexts(files, mode="b"):
            ddf = dd.read_csv("2014-01-*.csv")
    
            # since enforce is False, meta doesn't have to match computed
            assert (ddf.columns == ["name", "amount", "id"]).all()
>           assert (ddf.compute().columns == ["name", "amount", "id", "address"]).all()

dask/dataframe/io/tests/test_csv.py:1303: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:351: in _read_csv
    df = pandas_read_text(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:79: in pandas_read_text
    coerce_dtypes(df, dtypes)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

df = Empty DataFrame
Columns: [name, amount, id]
Index: []
dtypes = {'amount': dtype('int64'), 'id': dtype('int64'), 'name': dtype('O')}

    def coerce_dtypes(df, dtypes):
        """Coerce dataframe to dtypes safely
    
        Operates in place
    
        Parameters
        ----------
        df: Pandas DataFrame
        dtypes: dict like {'x': float}
        """
        bad_dtypes = []
        bad_dates = []
        errors = []
        for c in df.columns:
            if c in dtypes and df.dtypes[c] != dtypes[c]:
                actual = df.dtypes[c]
                desired = dtypes[c]
                if is_float_dtype(actual) and is_integer_dtype(desired):
                    bad_dtypes.append((c, actual, desired))
                elif is_object_dtype(actual) and is_datetime64_any_dtype(desired):
                    # This can only occur when parse_dates is specified, but an
                    # invalid date is encountered. Pandas then silently falls back
                    # to object dtype. Since `object_array.astype(datetime)` will
                    # silently overflow, error here and report.
                    bad_dates.append(c)
                else:
                    try:
                        df[c] = df[c].astype(dtypes[c])
                    except Exception as e:
                        bad_dtypes.append((c, actual, desired))
                        errors.append((c, e))
    
        if bad_dtypes:
            if errors:
                ex = "\n".join(
                    f"- {c}\n  {e!r}" for c, e in sorted(errors, key=lambda x: str(x[0]))
                )
                exceptions = (
                    "The following columns also raised exceptions on "
                    "conversion:\n\n%s\n\n"
                ) % ex
                extra = ""
            else:
                exceptions = ""
                # All mismatches are int->float, also suggest `assume_missing=True`
                extra = (
                    "\n\nAlternatively, provide `assume_missing=True` "
                    "to interpret\n"
                    "all unspecified integer columns as floats."
                )
    
            bad_dtypes = sorted(bad_dtypes, key=lambda x: str(x[0]))
            table = asciitable(["Column", "Found", "Expected"], bad_dtypes)
            dtype_kw = "dtype={%s}" % ",\n       ".join(
                f"{k!r}: '{v}'" for (k, v, _) in bad_dtypes
            )
    
            dtype_msg = (
                "{table}\n\n"
                "{exceptions}"
                "Usually this is due to dask's dtype inference failing, and\n"
                "*may* be fixed by specifying dtypes manually by adding:\n\n"
                "{dtype_kw}\n\n"
                "to the call to `read_csv`/`read_table`."
                "{extra}"
            ).format(table=table, exceptions=exceptions, dtype_kw=dtype_kw, extra=extra)
        else:
            dtype_msg = None
    
        if bad_dates:
            also = " also " if bad_dtypes else " "
            cols = "\n".join("- %s" % c for c in bad_dates)
            date_msg = (
                "The following columns{also}failed to properly parse as dates:\n\n"
                "{cols}\n\n"
                "This is usually due to an invalid value in that column. To\n"
                "diagnose and fix it's recommended to drop these columns from the\n"
                "`parse_dates` keyword, and manually convert them to dates later\n"
                "using `dd.to_datetime`."
            ).format(also=also, cols=cols)
        else:
            date_msg = None
    
        if bad_dtypes or bad_dates:
            rule = "\n\n%s\n\n" % ("-" * 61)
            msg = "Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n%s" % (
                rule.join(filter(None, [dtype_msg, date_msg]))
            )
>           raise ValueError(msg)
E           ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
E           
E           +--------+--------+----------+
E           | Column | Found  | Expected |
E           +--------+--------+----------+
E           | amount | object | int64    |
E           | id     | object | int64    |
E           +--------+--------+----------+
E           
E           The following columns also raised exceptions on conversion:
E           
E           - amount
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           - id
E             FutureWarning('ChainedAssignmentError: behaviour will change in pandas 3.0!\nYou are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\nA typical example is when you are setting values in a column of a DataFrame, like:\n\ndf["col"][row_indexer] = value\n\nUse `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')
E           
E           Usually this is due to dask's dtype inference failing, and
E           *may* be fixed by specifying dtypes manually by adding:
E           
E           dtype={'amount': 'object',
E                  'id': 'object'}
E           
E           to the call to `read_csv`/`read_table`.

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/csv.py:180: ValueError
____________________ test_reading_empty_csv_files_with_path ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_reading_empty_csv_files_with_path():
        with tmpdir() as tdir:
            for k, content in enumerate(["0, 1, 2", "", "6, 7, 8"]):
                with open(os.path.join(tdir, str(k) + ".csv"), "w") as file:
                    file.write(content)
            result = dd.read_csv(
                os.path.join(tdir, "*.csv"),
                include_path_column=True,
                converters={"path": parse_filename},
                names=["A", "B", "C"],
            ).compute()
            df = pd.DataFrame(
                {
                    "A": [0, 6],
                    "B": [1, 7],
                    "C": [2, 8],
                    "path": ["0.csv", "2.csv"],
                }
            )
>           df["path"] = df["path"].astype("category")

dask/dataframe/io/tests/test_csv.py:1793: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    A  B  C   path
0  0  1  2  0.csv
1  6  7  8  2.csv, key = 'path'
value = 0    0.csv
1    2.csv
Name: path, dtype: category
Categories (2, object): ['0.csv', '2.csv']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_read_json_with_path_column[split] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

orient = 'split'

    @pytest.mark.parametrize("orient", ["split", "records", "index", "columns", "values"])
    def test_read_json_with_path_column(orient):
        with tmpfile("json") as f:
            df.to_json(f, orient=orient, lines=False)
            actual = dd.read_json(f, orient=orient, lines=False, include_path_column=True)
            actual_pd = pd.read_json(f, orient=orient, lines=False)
            # The default column name when include_path_colum is True is "path"
            # The paths on Windows are converted to forward slash somewhere in the file
            # reading chain in Dask, so we have to do the same here.
>           actual_pd["path"] = pd.Series(
                (f.replace(os.sep, "/"),) * len(actual_pd), dtype="category"
            )

dask/dataframe/io/tests/test_json.py:29: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x  y
0  a  1
1  b  2
2  c  3
3  d  4, key = 'path'
value = 0    /tmp/tmpwy1yd46s.json
1    /tmp/tmpwy1yd46s.json
2    /tmp/tmpwy1yd46s.json
3    /tmp/tmpwy1yd46s.json
dtype: category
Categories (1, object): ['/tmp/tmpwy1yd46s.json']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_read_json_with_path_column[records] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

orient = 'records'

    @pytest.mark.parametrize("orient", ["split", "records", "index", "columns", "values"])
    def test_read_json_with_path_column(orient):
        with tmpfile("json") as f:
            df.to_json(f, orient=orient, lines=False)
            actual = dd.read_json(f, orient=orient, lines=False, include_path_column=True)
            actual_pd = pd.read_json(f, orient=orient, lines=False)
            # The default column name when include_path_colum is True is "path"
            # The paths on Windows are converted to forward slash somewhere in the file
            # reading chain in Dask, so we have to do the same here.
>           actual_pd["path"] = pd.Series(
                (f.replace(os.sep, "/"),) * len(actual_pd), dtype="category"
            )

dask/dataframe/io/tests/test_json.py:29: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x  y
0  a  1
1  b  2
2  c  3
3  d  4, key = 'path'
value = 0    /tmp/tmp3pufc1kc.json
1    /tmp/tmp3pufc1kc.json
2    /tmp/tmp3pufc1kc.json
3    /tmp/tmp3pufc1kc.json
dtype: category
Categories (1, object): ['/tmp/tmp3pufc1kc.json']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_read_json_with_path_column[index] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

orient = 'index'

    @pytest.mark.parametrize("orient", ["split", "records", "index", "columns", "values"])
    def test_read_json_with_path_column(orient):
        with tmpfile("json") as f:
            df.to_json(f, orient=orient, lines=False)
            actual = dd.read_json(f, orient=orient, lines=False, include_path_column=True)
            actual_pd = pd.read_json(f, orient=orient, lines=False)
            # The default column name when include_path_colum is True is "path"
            # The paths on Windows are converted to forward slash somewhere in the file
            # reading chain in Dask, so we have to do the same here.
>           actual_pd["path"] = pd.Series(
                (f.replace(os.sep, "/"),) * len(actual_pd), dtype="category"
            )

dask/dataframe/io/tests/test_json.py:29: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x  y
0  a  1
1  b  2
2  c  3
3  d  4, key = 'path'
value = 0    /tmp/tmpcppdutik.json
1    /tmp/tmpcppdutik.json
2    /tmp/tmpcppdutik.json
3    /tmp/tmpcppdutik.json
dtype: category
Categories (1, object): ['/tmp/tmpcppdutik.json']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_read_json_with_path_column[columns] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

orient = 'columns'

    @pytest.mark.parametrize("orient", ["split", "records", "index", "columns", "values"])
    def test_read_json_with_path_column(orient):
        with tmpfile("json") as f:
            df.to_json(f, orient=orient, lines=False)
            actual = dd.read_json(f, orient=orient, lines=False, include_path_column=True)
            actual_pd = pd.read_json(f, orient=orient, lines=False)
            # The default column name when include_path_colum is True is "path"
            # The paths on Windows are converted to forward slash somewhere in the file
            # reading chain in Dask, so we have to do the same here.
>           actual_pd["path"] = pd.Series(
                (f.replace(os.sep, "/"),) * len(actual_pd), dtype="category"
            )

dask/dataframe/io/tests/test_json.py:29: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x  y
0  a  1
1  b  2
2  c  3
3  d  4, key = 'path'
value = 0    /tmp/tmp2e32vcqp.json
1    /tmp/tmp2e32vcqp.json
2    /tmp/tmp2e32vcqp.json
3    /tmp/tmp2e32vcqp.json
dtype: category
Categories (1, object): ['/tmp/tmp2e32vcqp.json']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_read_json_with_path_column[values] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

orient = 'values'

    @pytest.mark.parametrize("orient", ["split", "records", "index", "columns", "values"])
    def test_read_json_with_path_column(orient):
        with tmpfile("json") as f:
            df.to_json(f, orient=orient, lines=False)
            actual = dd.read_json(f, orient=orient, lines=False, include_path_column=True)
            actual_pd = pd.read_json(f, orient=orient, lines=False)
            # The default column name when include_path_colum is True is "path"
            # The paths on Windows are converted to forward slash somewhere in the file
            # reading chain in Dask, so we have to do the same here.
>           actual_pd["path"] = pd.Series(
                (f.replace(os.sep, "/"),) * len(actual_pd), dtype="category"
            )

dask/dataframe/io/tests/test_json.py:29: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    0  1
0  a  1
1  b  2
2  c  3
3  d  4, key = 'path'
value = 0    /tmp/tmppffyeai5.json
1    /tmp/tmppffyeai5.json
2    /tmp/tmppffyeai5.json
3    /tmp/tmppffyeai5.json
dtype: category
Categories (1, object): ['/tmp/tmppffyeai5.json']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_read_json_with_path_converter ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_read_json_with_path_converter():
        path_column_name = "filenames"
    
        def path_converter(x):
            return "asdf.json"
    
        with tmpfile("json") as f:
            df.to_json(f, orient="records", lines=False)
            actual = dd.read_json(
                f,
                orient="records",
                lines=False,
                include_path_column=path_column_name,
                path_converter=path_converter,
            )
            actual_pd = pd.read_json(f, orient="records", lines=False)
>           actual_pd[path_column_name] = pd.Series(
                (path_converter(f),) * len(actual_pd), dtype="category"
            )

dask/dataframe/io/tests/test_json.py:59: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x  y
0  a  1
1  b  2
2  c  3
3  d  4, key = 'filenames'
value = 0    asdf.json
1    asdf.json
2    asdf.json
3    asdf.json
dtype: category
Categories (1, object): ['asdf.json']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_to_datetime_timezone ___________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_to_datetime_timezone():
        pdf = pd.DataFrame({"a": ["2023-04-01 22:12:11.9324171+00:00"]})
        df = from_pandas(pdf)
>       df["a"] = to_datetime(df.a, format="ISO8601", meta=("a", "datetime64[ns, UTC]"))

dask/dataframe/dask_expr/tests/test_collection.py:826: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = 'a'
value = Series([], Name: a, dtype: datetime64[ns, UTC])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_categorical[pyarrow-pyarrow] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

args = ('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_categorical_pyarrow_pyarr0',)
kwargs = {'categories': 'x', 'engine': 'pyarrow'}
func = <function read_parquet at 0x7f5bf9b685c0>
exc = FutureWarning('An error occurred while calling the read_parquet method registered to the pandas backend.\nOriginal Mes...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
>           return func(*args, **kwargs)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:140: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5372: in read_parquet
    ReadParquetFSSpec(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:844: in __new__
    inst = super().__new__(cls, *args, _determ_token=_determ_token, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:72: in __new__
    inst._name
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:790: in _name
    return self._funcname + "-" + self.deterministic_token
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:517: in deterministic_token
    self._determ_token = self.__dask_tokenize__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:784: in __dask_tokenize__
    funcname(type(self)), self.checksum, *self.operands[:-1]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:794: in checksum
    return self._dataset_info["checksum"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:1398: in _dataset_info
    meta = self.engine._create_dd_meta(dataset_info)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:1211: in _create_dd_meta
    meta = clear_known_categories(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:250: in clear_known_categories
    x[c] = x[c].cat.set_categories([UNKNOWN_CATEGORIES])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'x'
value = Series([], Name: x, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning

The above exception was the direct cause of the following exception:

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_categorical_pyarrow_pyarr0')
write_engine = 'pyarrow', read_engine = 'pyarrow'

    def test_categorical(tmpdir, write_engine, read_engine):
        tmp = str(tmpdir)
        df = pd.DataFrame({"x": ["a", "b", "c"] * 100}, dtype="category")
        ddf = dd.from_pandas(df, npartitions=3)
        dd.to_parquet(ddf, tmp, engine=write_engine)
    
>       ddf2 = dd.read_parquet(tmp, categories="x", engine=read_engine)

dask/dataframe/io/tests/test_parquet.py:573: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = ('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_categorical_pyarrow_pyarr0',)
kwargs = {'categories': 'x', 'engine': 'pyarrow'}
func = <function read_parquet at 0x7f5bf9b685c0>
exc = FutureWarning('An error occurred while calling the read_parquet method registered to the pandas backend.\nOriginal Mes...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
            return func(*args, **kwargs)
        except Exception as e:
            try:
                exc = type(e)(
                    f"An error occurred while calling the {funcname(func)} "
                    f"method registered to the {self.backend} backend.\n"
                    f"Original Message: {e}"
                )
            except TypeError:
                raise e
            else:
>               raise exc from e
E               FutureWarning: An error occurred while calling the read_parquet method registered to the pandas backend.
E               Original Message: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:151: FutureWarning
__________________________ test_append_with_partition __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_append_with_partition0')

    @PYARROW_MARK
    def test_append_with_partition(tmpdir):
        tmp = str(tmpdir)
        df0 = pd.DataFrame(
            {
                "lat": np.arange(0, 10, dtype="int64"),
                "lon": np.arange(10, 20, dtype="int64"),
                "value": np.arange(100, 110, dtype="int64"),
            }
        )
        df0.index.name = "index"
        df1 = pd.DataFrame(
            {
                "lat": np.arange(10, 20, dtype="int64"),
                "lon": np.arange(10, 20, dtype="int64"),
                "value": np.arange(120, 130, dtype="int64"),
            }
        )
        df1.index.name = "index"
    
        # Check that nullable dtypes work
        # (see: https://github.com/dask/dask/issues/8373)
>       df0["lat"] = df0["lat"].astype("Int64")

dask/dataframe/io/tests/test_parquet.py:666: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        lat  lon  value
index                 
0        0   10    100
1        1   11    101
2        2   12    102
3  ... 104
5        5   15    105
6        6   16    106
7        7   17    107
8        8   18    108
9        9   19    109
key = 'lat'
value = index
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
Name: lat, dtype: Int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_partition_on_cats_2[pyarrow] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_partition_on_cats_2_pyarr0')
engine = 'pyarrow'

    def test_partition_on_cats_2(tmpdir, engine):
        tmp = str(tmpdir)
        d = pd.DataFrame(
            {
                "a": np.random.rand(50),
                "b": np.random.choice(["x", "y", "z"], size=50),
                "c": np.random.choice(["x", "y", "z"], size=50),
            }
        )
        d = dd.from_pandas(d, 2)
        d.to_parquet(tmp, partition_on=["b", "c"], engine=engine)
        df = dd.read_parquet(tmp, engine=engine)
        assert set(df.b.cat.categories) == {"x", "y", "z"}
        assert set(df.c.cat.categories) == {"x", "y", "z"}
    
        df = dd.read_parquet(tmp, columns=["a", "c"], engine=engine)
        assert set(df.c.cat.categories) == {"x", "y", "z"}
        assert "b" not in df.columns
>       assert_eq(df, df.compute())

dask/dataframe/io/tests/test_parquet.py:763: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =                             a  c  b
__null_dask_index__                
1                    0.610908  z  z
9                    0.533478  z  z
key = 'c'
value = __null_dask_index__
1    z
9    z
dtype: category
Categories (3, object): ['x', 'y', 'z']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_categories[pyarrow] ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_categories_pyarrow_0')
engine = 'pyarrow'

    def test_categories(tmpdir, engine):
        fn = str(tmpdir)
        df = pd.DataFrame({"x": [1, 2, 3, 4, 5], "y": list("caaab")})
    
        ctx = contextlib.nullcontext
        with ctx({"dataframe.convert-string": False}):
            ddf = dd.from_pandas(df, npartitions=2)
>           ddf["y"] = ddf.y.astype("category")

dask/dataframe/io/tests/test_parquet.py:1068: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_pyarrow_schema_inference[infer-True] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = '/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_pyarrow_schema_inference_1'
index = True, schema = 'infer'

    @PYARROW_MARK
    @pytest.mark.parametrize("index", [False, True])
    @pytest.mark.parametrize("schema", ["infer", "complex"])
    def test_pyarrow_schema_inference(tmpdir, index, schema):
        if schema == "complex":
            schema = {"index": pa.string(), "amount": pa.int64()}
    
        tmpdir = str(tmpdir)
        df = pd.DataFrame(
            {
                "index": ["1", "2", "3", "2", "3", "1", "4"],
                "date": pd.to_datetime(
                    [
                        "2017-01-01",
                        "2017-01-01",
                        "2017-01-01",
                        "2017-01-02",
                        "2017-01-02",
                        "2017-01-06",
                        "2017-01-09",
                    ],
                    unit=None if not PANDAS_GE_300 else "ms",
                ),
                "amount": [100, 200, 300, 400, 500, 600, 700],
            },
            index=range(7, 14),
        )
        if index:
            df = dd.from_pandas(df, npartitions=2).set_index("index")
        else:
            df = dd.from_pandas(df, npartitions=2)
    
>       df.to_parquet(tmpdir, schema=schema)

dask/dataframe/io/tests/test_parquet.py:1255: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3312: in to_parquet
    return to_parquet(self, path, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:661: in to_parquet
    out = out.compute(**compute_kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [index, date, amount]
Index: []
key = '_partitions', value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_pyarrow_schema_inference[complex-True] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = '/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_pyarrow_schema_inference_3'
index = True, schema = {'amount': DataType(int64), 'index': DataType(string)}

    @PYARROW_MARK
    @pytest.mark.parametrize("index", [False, True])
    @pytest.mark.parametrize("schema", ["infer", "complex"])
    def test_pyarrow_schema_inference(tmpdir, index, schema):
        if schema == "complex":
            schema = {"index": pa.string(), "amount": pa.int64()}
    
        tmpdir = str(tmpdir)
        df = pd.DataFrame(
            {
                "index": ["1", "2", "3", "2", "3", "1", "4"],
                "date": pd.to_datetime(
                    [
                        "2017-01-01",
                        "2017-01-01",
                        "2017-01-01",
                        "2017-01-02",
                        "2017-01-02",
                        "2017-01-06",
                        "2017-01-09",
                    ],
                    unit=None if not PANDAS_GE_300 else "ms",
                ),
                "amount": [100, 200, 300, 400, 500, 600, 700],
            },
            index=range(7, 14),
        )
        if index:
            df = dd.from_pandas(df, npartitions=2).set_index("index")
        else:
            df = dd.from_pandas(df, npartitions=2)
    
>       df.to_parquet(tmpdir, schema=schema)

dask/dataframe/io/tests/test_parquet.py:1255: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3312: in to_parquet
    return to_parquet(self, path, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:661: in to_parquet
    out = out.compute(**compute_kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [index, date, amount]
Index: []
key = '_partitions', value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_partition_on[pyarrow] __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = '/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_partition_on_pyarrow_0'
engine = 'pyarrow'

    def test_partition_on(tmpdir, engine):
        tmpdir = str(tmpdir)
        df = pd.DataFrame(
            {
                "a1": np.random.choice(["A", "B", "C"], size=100),
                "a2": np.random.choice(["X", "Y", "Z"], size=100),
                "b": np.random.random(size=100),
                "c": np.random.randint(1, 5, size=100),
                "d": np.arange(0, 100),
            }
        )
        d = dd.from_pandas(df, npartitions=2)
        d.to_parquet(tmpdir, partition_on=["a1", "a2"], engine=engine)
        # Note #1: Cross-engine functionality is missing
        # Note #2: The index is not preserved in pyarrow when partition_on is used
        out = dd.read_parquet(
            tmpdir, engine=engine, index=False, calculate_divisions=False
>       ).compute()

dask/dataframe/io/tests/test_parquet.py:1310: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =           b  c   d a1 a2
0  0.193015  3  11  B  Y
1  0.809473  2  13  B  Y
2  0.165749  1  20  B  Y
3  0.982045  4  21...940176  3  30  B  Y
6  0.887818  3  41  B  Y
7  0.394629  3  43  B  Y
8  0.372469  3  44  B  Y
9  0.364153  4  48  B  Y
key = 'a1'
value = 0    B
1    B
2    B
3    B
4    B
5    B
6    B
7    B
8    B
9    B
dtype: category
Categories (3, object): ['A', 'B', 'C']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_partition_on_duplicates[pyarrow] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = '/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_partition_on_duplicates_p0'
engine = 'pyarrow'

    def test_partition_on_duplicates(tmpdir, engine):
        # https://github.com/dask/dask/issues/6445
        tmpdir = str(tmpdir)
        df = pd.DataFrame(
            {
                "a1": np.random.choice(["A", "B", "C"], size=100),
                "a2": np.random.choice(["X", "Y", "Z"], size=100),
                "data": np.random.random(size=100),
            }
        )
        d = dd.from_pandas(df, npartitions=2)
    
        for _ in range(2):
            d.to_parquet(tmpdir, partition_on=["a1", "a2"], engine=engine)
    
>       out = dd.read_parquet(tmpdir, engine=engine).compute()

dask/dataframe/io/tests/test_parquet.py:1335: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =                          data a1 a2
__null_dask_index__                
53                   0.050520  B  Y
55        ...49343  B  Y
92                   0.108151  B  Y
96                   0.048550  B  Y
97                   0.651192  B  Y
key = 'a1'
value = __null_dask_index__
53    B
55    B
57    B
70    B
84    B
85    B
88    B
92    B
96    B
97    B
dtype: category
Categories (3, object): ['A', 'B', 'C']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_partition_on_string[aa] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = '/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_partition_on_string_aa_0'
partition_on = 'aa'

    @PYARROW_MARK
    @pytest.mark.parametrize("partition_on", ["aa", ["aa"]])
    def test_partition_on_string(tmpdir, partition_on):
        tmpdir = str(tmpdir)
        with dask.config.set(scheduler="single-threaded"):
            tmpdir = str(tmpdir)
            df = pd.DataFrame(
                {
                    "aa": np.random.choice(["A", "B", "C"], size=100),
                    "bb": np.random.random(size=100),
                    "cc": np.random.randint(1, 5, size=100),
                }
            )
            d = dd.from_pandas(df, npartitions=2)
            d.to_parquet(tmpdir, partition_on=partition_on, write_index=False)
            out = dd.read_parquet(tmpdir, index=False, calculate_divisions=False)
    
>       out = out.compute()

dask/dataframe/io/tests/test_parquet.py:1365: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =           bb  cc aa
0   0.667245   3  C
1   0.640090   3  C
2   0.153457   2  C
3   0.206747   3  C
4   0.128230   3  ...10  0.661131   3  C
11  0.836594   1  C
12  0.432227   3  C
13  0.267061   4  C
14  0.440144   4  C
15  0.186746   2  C
key = 'aa'
value = 0     C
1     C
2     C
3     C
4     C
5     C
6     C
7     C
8     C
9     C
10    C
11    C
12    C
13    C
14    C
15    C
dtype: category
Categories (3, object): ['A', 'B', 'C']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_partition_on_string[partition_on1] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = '/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_partition_on_string_parti0'
partition_on = ['aa']

    @PYARROW_MARK
    @pytest.mark.parametrize("partition_on", ["aa", ["aa"]])
    def test_partition_on_string(tmpdir, partition_on):
        tmpdir = str(tmpdir)
        with dask.config.set(scheduler="single-threaded"):
            tmpdir = str(tmpdir)
            df = pd.DataFrame(
                {
                    "aa": np.random.choice(["A", "B", "C"], size=100),
                    "bb": np.random.random(size=100),
                    "cc": np.random.randint(1, 5, size=100),
                }
            )
            d = dd.from_pandas(df, npartitions=2)
            d.to_parquet(tmpdir, partition_on=partition_on, write_index=False)
            out = dd.read_parquet(tmpdir, index=False, calculate_divisions=False)
    
>       out = out.compute()

dask/dataframe/io/tests/test_parquet.py:1365: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =           bb  cc aa
0   0.053349   4  C
1   0.791488   4  C
2   0.099491   3  C
3   0.822764   2  C
4   0.675456   1  ...8   0.130611   2  C
9   0.962954   1  C
10  0.332279   1  C
11  0.900726   4  C
12  0.117107   1  C
13  0.299347   3  C
key = 'aa'
value = 0     C
1     C
2     C
3     C
4     C
5     C
6     C
7     C
8     C
9     C
10    C
11    C
12    C
13    C
dtype: category
Categories (3, object): ['A', 'B', 'C']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_filters_categorical[pyarrow-pyarrow] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = '/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_filters_categorical_pyarr0'
write_engine = 'pyarrow', read_engine = 'pyarrow'

    def test_filters_categorical(tmpdir, write_engine, read_engine):
        tmpdir = str(tmpdir)
        cats = ["2018-01-01", "2018-01-02", "2018-01-03", "2018-01-04"]
        dftest = pd.DataFrame(
            {
                "dummy": [1, 1, 1, 1],
                "DatePart": pd.Categorical(cats, categories=cats, ordered=True),
            }
        )
        ddftest = dd.from_pandas(dftest, npartitions=4).set_index("dummy")
>       ddftest.to_parquet(tmpdir, partition_on="DatePart", engine=write_engine)

dask/dataframe/io/tests/test_parquet.py:1380: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3312: in to_parquet
    return to_parquet(self, path, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:661: in to_parquet
    out = out.compute(**compute_kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dummy, DatePart]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_parquet_select_cats[pyarrow] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

args = ('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_parquet_select_cats_pyarr0',)
kwargs = {'columns': ['ints'], 'engine': 'pyarrow'}
func = <function read_parquet at 0x7f5bf9b685c0>
exc = FutureWarning('An error occurred while calling the read_parquet method registered to the pandas backend.\nOriginal Mes...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
>           return func(*args, **kwargs)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:140: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5372: in read_parquet
    ReadParquetFSSpec(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:844: in __new__
    inst = super().__new__(cls, *args, _determ_token=_determ_token, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:72: in __new__
    inst._name
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:790: in _name
    return self._funcname + "-" + self.deterministic_token
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:517: in deterministic_token
    self._determ_token = self.__dask_tokenize__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:784: in __dask_tokenize__
    funcname(type(self)), self.checksum, *self.operands[:-1]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:794: in checksum
    return self._dataset_info["checksum"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:1398: in _dataset_info
    meta = self.engine._create_dd_meta(dataset_info)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:1211: in _create_dd_meta
    meta = clear_known_categories(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:250: in clear_known_categories
    x[c] = x[c].cat.set_categories([UNKNOWN_CATEGORIES])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [categories, ints, floats]
Index: []
key = 'categories'
value = Series([], Name: categories, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning

The above exception was the direct cause of the following exception:

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_parquet_select_cats_pyarr0')
engine = 'pyarrow'

    def test_parquet_select_cats(tmpdir, engine):
        fn = str(tmpdir)
        df = pd.DataFrame(
            {
                "categories": pd.Series(
                    np.random.choice(["a", "b", "c", "d", "e", "f"], size=100),
                    dtype="category",
                ),
                "ints": pd.Series(list(range(0, 100)), dtype="int"),
                "floats": pd.Series(list(range(0, 100)), dtype="float"),
            }
        )
    
        ddf = dd.from_pandas(df, 1)
        ddf.to_parquet(fn, engine=engine)
>       rddf = dd.read_parquet(fn, columns=["ints"], engine=engine)

dask/dataframe/io/tests/test_parquet.py:1613: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = ('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_parquet_select_cats_pyarr0',)
kwargs = {'columns': ['ints'], 'engine': 'pyarrow'}
func = <function read_parquet at 0x7f5bf9b685c0>
exc = FutureWarning('An error occurred while calling the read_parquet method registered to the pandas backend.\nOriginal Mes...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
            return func(*args, **kwargs)
        except Exception as e:
            try:
                exc = type(e)(
                    f"An error occurred while calling the {funcname(func)} "
                    f"method registered to the {self.backend} backend.\n"
                    f"Original Message: {e}"
                )
            except TypeError:
                raise e
            else:
>               raise exc from e
E               FutureWarning: An error occurred while calling the read_parquet method registered to the pandas backend.
E               Original Message: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:151: FutureWarning
__________________ test_writing_parquet_with_kwargs[pyarrow] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_writing_parquet_with_kwar0')
engine = 'pyarrow'

    def test_writing_parquet_with_kwargs(tmpdir, engine):
        fn = str(tmpdir)
        path1 = os.path.join(fn, "normal")
        path2 = os.path.join(fn, "partitioned")
    
        df = pd.DataFrame(
            {
                "a": np.random.choice(["A", "B", "C"], size=100),
                "b": np.random.random(size=100),
                "c": np.random.randint(1, 5, size=100),
            }
        )
        df.index.name = "index"
        ddf = dd.from_pandas(df, npartitions=3)
    
        write_kwargs = {
            "pyarrow": {
                "compression": "snappy",
                "coerce_timestamps": None,
                "use_dictionary": True,
            },
        }
    
        ddf.to_parquet(path1, engine=engine, **write_kwargs[engine])
        out = dd.read_parquet(path1, engine=engine, calculate_divisions=True)
        assert_eq(out, ddf, check_index=True)
    
        # Avoid race condition in pyarrow 0.8.0 on writing partitioned datasets
        with dask.config.set(scheduler="sync"):
            ddf.to_parquet(path2, engine=engine, partition_on=["a"], **write_kwargs[engine])
>       out = dd.read_parquet(path2, engine=engine).compute()

dask/dataframe/io/tests/test_parquet.py:1800: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =               b  c  a
index                
36     0.218269  2  C
38     0.236798  1  C
40     0.271178  3  C
42     0...009  3  C
52     0.334857  1  C
53     0.464266  3  C
56     0.089878  3  C
62     0.636040  4  C
64     0.996734  2  C
key = 'a'
value = index
36    C
38    C
40    C
42    C
43    C
45    C
47    C
50    C
52    C
53    C
56    C
62    C
64    C
dtype: category
Categories (3, object): ['A', 'B', 'C']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_select_partitioned_column[pyarrow] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_select_partitioned_column0')
engine = 'pyarrow'

    def test_select_partitioned_column(tmpdir, engine):
        fn = str(tmpdir)
        size = 20
        d = {
            "signal1": np.random.normal(0, 0.3, size=size).cumsum() + 50,
            "fake_categorical1": np.random.choice(["A", "B", "C"], size=size),
            "fake_categorical2": np.random.choice(["D", "E", "F"], size=size),
        }
        df = dd.from_pandas(pd.DataFrame(d), 2)
        df.to_parquet(
            fn,
            compression="snappy",
            write_index=False,
            engine=engine,
            partition_on=["fake_categorical1", "fake_categorical2"],
        )
    
        df_partitioned = dd.read_parquet(fn, engine=engine)
>       df_partitioned[df_partitioned.fake_categorical1 == "A"].compute()

dask/dataframe/io/tests/test_parquet.py:1851: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      signal1 fake_categorical1 fake_categorical2
0  49.561189                 C                 E
1  49.461769                 C                 E
key = 'fake_categorical1'
value = 0    C
1    C
dtype: category
Categories (3, object): ['A', 'B', 'C']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_arrow_partitioning ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_arrow_partitioning0')

    @PYARROW_MARK
    def test_arrow_partitioning(tmpdir):
        # Issue #3518
        path = str(tmpdir)
        data = {
            "p": np.repeat(np.arange(3), 2).astype(np.int8),
            "b": np.repeat(-1, 6).astype(np.int16),
            "c": np.repeat(-2, 6).astype(np.float32),
            "d": np.repeat(-3, 6).astype(np.float64),
        }
        pdf = pd.DataFrame(data)
        ddf = dd.from_pandas(pdf, npartitions=2)
        ddf.to_parquet(path, write_index=False, partition_on="p")
    
        ddf = dd.read_parquet(path, index=False)
    
>       ddf.astype({"b": np.float32}).compute()

dask/dataframe/io/tests/test_parquet.py:1870: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    b    c    d  p
0 -1 -2.0 -3.0  1, key = 'p'
value = 0    1
dtype: category
Categories (3, int32): [0, 1, 2]

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_append_cat_fp[pyarrow] __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_append_cat_fp_pyarrow_0')
engine = 'pyarrow'

    def test_append_cat_fp(tmpdir, engine):
        path = str(tmpdir)
        # https://github.com/dask/dask/issues/4120
        df = pd.DataFrame({"x": ["a", "a", "b", "a", "b"]})
>       df["x"] = df["x"].astype("category")

dask/dataframe/io/tests/test_parquet.py:1882: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x
0  a
1  a
2  b
3  a
4  b, key = 'x'
value = 0    a
1    a
2    b
3    a
4    b
Name: x, dtype: category
Categories (2, object): ['a', 'b']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_roundtrip_arrow[df3] ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

args = ('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_roundtrip_arrow_df3_0',)
kwargs = {'calculate_divisions': True}
func = <function read_parquet at 0x7f5bf9b685c0>
exc = FutureWarning('An error occurred while calling the read_parquet method registered to the pandas backend.\nOriginal Mes...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
>           return func(*args, **kwargs)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:140: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5372: in read_parquet
    ReadParquetFSSpec(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:844: in __new__
    inst = super().__new__(cls, *args, _determ_token=_determ_token, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:72: in __new__
    inst._name
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:790: in _name
    return self._funcname + "-" + self.deterministic_token
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:517: in deterministic_token
    self._determ_token = self.__dask_tokenize__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:784: in __dask_tokenize__
    funcname(type(self)), self.checksum, *self.operands[:-1]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:794: in checksum
    return self._dataset_info["checksum"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:1398: in _dataset_info
    meta = self.engine._create_dd_meta(dataset_info)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:1211: in _create_dd_meta
    meta = clear_known_categories(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:250: in clear_known_categories
    x[c] = x[c].cat.set_categories([UNKNOWN_CATEGORIES])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'x'
value = Series([], Name: x, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning

The above exception was the direct cause of the following exception:

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_roundtrip_arrow_df3_0')
df =        x
index   
0      a
1      b
2      a

    @PYARROW_MARK
    @pytest.mark.parametrize(
        "df",
        [
            pd.DataFrame({"x": [4, 5, 6, 1, 2, 3]}),
            pd.DataFrame({"x": ["c", "a", "b"]}),
            pd.DataFrame({"x": ["cc", "a", "bbb"]}),
            pytest.param(pd.DataFrame({"x": pd.Categorical(["a", "b", "a"])})),
            pytest.param(pd.DataFrame({"x": pd.Categorical([1, 2, 1])})),
            pd.DataFrame({"x": list(map(pd.Timestamp, [3000000, 2000000, 1000000]))}),  # ms
            pd.DataFrame({"x": list(map(pd.Timestamp, [3000, 2000, 1000]))}),  # us
            pd.DataFrame({"x": [3000, 2000, 1000]}).astype("M8[ns]"),
            # pd.DataFrame({'x': [3, 2, 1]}).astype('M8[ns]'), # Casting errors
            pd.DataFrame({"x": [3, 2, 1]}).astype("M8[us]"),
            pd.DataFrame({"x": [3, 2, 1]}).astype("M8[ms]"),
            pd.DataFrame({"x": [3, 2, 1]}).astype("uint16"),
            pd.DataFrame({"x": [3, 2, 1]}).astype("float32"),
            pd.DataFrame({"x": [3, 1, 2]}, index=[3, 2, 1]),
            pd.DataFrame(
                {"x": [4, 5, 6, 1, 2, 3]}, index=pd.Index([1, 2, 3, 4, 5, 6], name="foo")
            ),
            pd.DataFrame({"x": [1, 2, 3], "y": [3, 2, 1]}),
            pd.DataFrame({"x": [1, 2, 3], "y": [3, 2, 1]}, columns=["y", "x"]),
            pd.DataFrame({"0": [3, 2, 1]}),
            pd.DataFrame({"x": [3, 2, None]}),
            pd.DataFrame({"-": [3.0, 2.0, None]}),
            pd.DataFrame({".": [3.0, 2.0, None]}),
            pd.DataFrame({" ": [3.0, 2.0, None]}),
        ],
    )
    def test_roundtrip_arrow(tmpdir, df):
        # Index will be given a name when preserved as index
        tmp_path = str(tmpdir)
        if not df.index.name:
            df.index.name = "index"
        ddf = dd.from_pandas(df, npartitions=2)
        dd.to_parquet(ddf, tmp_path, write_index=True)
>       ddf2 = dd.read_parquet(tmp_path, calculate_divisions=True)

dask/dataframe/io/tests/test_parquet.py:1929: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = ('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_roundtrip_arrow_df3_0',)
kwargs = {'calculate_divisions': True}
func = <function read_parquet at 0x7f5bf9b685c0>
exc = FutureWarning('An error occurred while calling the read_parquet method registered to the pandas backend.\nOriginal Mes...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
            return func(*args, **kwargs)
        except Exception as e:
            try:
                exc = type(e)(
                    f"An error occurred while calling the {funcname(func)} "
                    f"method registered to the {self.backend} backend.\n"
                    f"Original Message: {e}"
                )
            except TypeError:
                raise e
            else:
>               raise exc from e
E               FutureWarning: An error occurred while calling the read_parquet method registered to the pandas backend.
E               Original Message: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:151: FutureWarning
__________________________ test_roundtrip_arrow[df4] ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

args = ('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_roundtrip_arrow_df4_0',)
kwargs = {'calculate_divisions': True}
func = <function read_parquet at 0x7f5bf9b685c0>
exc = FutureWarning('An error occurred while calling the read_parquet method registered to the pandas backend.\nOriginal Mes...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
>           return func(*args, **kwargs)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:140: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5372: in read_parquet
    ReadParquetFSSpec(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:844: in __new__
    inst = super().__new__(cls, *args, _determ_token=_determ_token, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:72: in __new__
    inst._name
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:790: in _name
    return self._funcname + "-" + self.deterministic_token
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:517: in deterministic_token
    self._determ_token = self.__dask_tokenize__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:784: in __dask_tokenize__
    funcname(type(self)), self.checksum, *self.operands[:-1]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:794: in checksum
    return self._dataset_info["checksum"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:1398: in _dataset_info
    meta = self.engine._create_dd_meta(dataset_info)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:1211: in _create_dd_meta
    meta = clear_known_categories(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:250: in clear_known_categories
    x[c] = x[c].cat.set_categories([UNKNOWN_CATEGORIES])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'x'
value = Series([], Name: x, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning

The above exception was the direct cause of the following exception:

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_roundtrip_arrow_df4_0')
df =        x
index   
0      1
1      2
2      1

    @PYARROW_MARK
    @pytest.mark.parametrize(
        "df",
        [
            pd.DataFrame({"x": [4, 5, 6, 1, 2, 3]}),
            pd.DataFrame({"x": ["c", "a", "b"]}),
            pd.DataFrame({"x": ["cc", "a", "bbb"]}),
            pytest.param(pd.DataFrame({"x": pd.Categorical(["a", "b", "a"])})),
            pytest.param(pd.DataFrame({"x": pd.Categorical([1, 2, 1])})),
            pd.DataFrame({"x": list(map(pd.Timestamp, [3000000, 2000000, 1000000]))}),  # ms
            pd.DataFrame({"x": list(map(pd.Timestamp, [3000, 2000, 1000]))}),  # us
            pd.DataFrame({"x": [3000, 2000, 1000]}).astype("M8[ns]"),
            # pd.DataFrame({'x': [3, 2, 1]}).astype('M8[ns]'), # Casting errors
            pd.DataFrame({"x": [3, 2, 1]}).astype("M8[us]"),
            pd.DataFrame({"x": [3, 2, 1]}).astype("M8[ms]"),
            pd.DataFrame({"x": [3, 2, 1]}).astype("uint16"),
            pd.DataFrame({"x": [3, 2, 1]}).astype("float32"),
            pd.DataFrame({"x": [3, 1, 2]}, index=[3, 2, 1]),
            pd.DataFrame(
                {"x": [4, 5, 6, 1, 2, 3]}, index=pd.Index([1, 2, 3, 4, 5, 6], name="foo")
            ),
            pd.DataFrame({"x": [1, 2, 3], "y": [3, 2, 1]}),
            pd.DataFrame({"x": [1, 2, 3], "y": [3, 2, 1]}, columns=["y", "x"]),
            pd.DataFrame({"0": [3, 2, 1]}),
            pd.DataFrame({"x": [3, 2, None]}),
            pd.DataFrame({"-": [3.0, 2.0, None]}),
            pd.DataFrame({".": [3.0, 2.0, None]}),
            pd.DataFrame({" ": [3.0, 2.0, None]}),
        ],
    )
    def test_roundtrip_arrow(tmpdir, df):
        # Index will be given a name when preserved as index
        tmp_path = str(tmpdir)
        if not df.index.name:
            df.index.name = "index"
        ddf = dd.from_pandas(df, npartitions=2)
        dd.to_parquet(ddf, tmp_path, write_index=True)
>       ddf2 = dd.read_parquet(tmp_path, calculate_divisions=True)

dask/dataframe/io/tests/test_parquet.py:1929: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = ('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_roundtrip_arrow_df4_0',)
kwargs = {'calculate_divisions': True}
func = <function read_parquet at 0x7f5bf9b685c0>
exc = FutureWarning('An error occurred while calling the read_parquet method registered to the pandas backend.\nOriginal Mes...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
            return func(*args, **kwargs)
        except Exception as e:
            try:
                exc = type(e)(
                    f"An error occurred while calling the {funcname(func)} "
                    f"method registered to the {self.backend} backend.\n"
                    f"Original Message: {e}"
                )
            except TypeError:
                raise e
            else:
>               raise exc from e
E               FutureWarning: An error occurred while calling the read_parquet method registered to the pandas backend.
E               Original Message: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:151: FutureWarning
___________________________ test_columns_assignment ____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_columns_assignment():
        df = pd.DataFrame({"x": [1, 2, 3, 4]})
        ddf = from_pandas(df, npartitions=2)
    
        df2 = df.assign(y=df.x + 1, z=df.x - 1)
        df[["a", "b"]] = df2[["y", "z"]]
    
>       ddf2 = ddf.assign(y=ddf.x + 1, z=ddf.x - 1)

dask/dataframe/dask_expr/tests/test_collection.py:1436: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'y'
value = Series([], Name: x, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_parent_mixed_column_assignment ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_parent_mixed_column_assignment():
        pdf = pd.DataFrame(list(range(20)))
        df = from_pandas(pdf, npartitions=4)
>       df["a"] = 0

dask/dataframe/dask_expr/tests/test_collection.py:1445: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = 'a', value = 0

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_setitem_triggering_realign ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_setitem_triggering_realign():
        a = from_pandas(pd.DataFrame({"A": range(12)}), npartitions=3)
        b = from_pandas(pd.Series(range(12), name="B"), npartitions=4)
>       a["C"] = b

dask/dataframe/dask_expr/tests/test_collection.py:1496: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2814: in assign
    result = new_collection(expr.AssignAlign(result, k, v.expr))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:3520: in _meta
    return self._expr_cls(*self.operands)._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A]
Index: [], key = 'C'
value = Series([], Name: B, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_timeseries_nulls_in_schema[pyarrow-infer] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_timeseries_nulls_in_schem0')
engine = 'pyarrow', schema = 'infer'

    @pytest.mark.parametrize("schema", ["infer", None])
    def test_timeseries_nulls_in_schema(tmpdir, engine, schema):
        # GH#5608: relative path failing _metadata/_common_metadata detection.
        tmp_path = str(tmpdir.mkdir("files"))
        tmp_path = os.path.join(tmp_path, "../", "files")
    
        ddf2 = (
            dask.datasets.timeseries(start="2000-01-01", end="2000-01-03", freq="1h")
            .reset_index()
            .map_partitions(lambda x: x.loc[:5])
        )
>       ddf2 = ddf2.set_index("x").reset_index().persist()

dask/dataframe/io/tests/test_parquet.py:2013: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:457: in persist
    out = self.optimize(fuse=fuse)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [timestamp, name, id, x, y]
Index: []
key = '_partitions', value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_timeseries_nulls_in_schema[pyarrow-None] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_timeseries_nulls_in_schem1')
engine = 'pyarrow', schema = None

    @pytest.mark.parametrize("schema", ["infer", None])
    def test_timeseries_nulls_in_schema(tmpdir, engine, schema):
        # GH#5608: relative path failing _metadata/_common_metadata detection.
        tmp_path = str(tmpdir.mkdir("files"))
        tmp_path = os.path.join(tmp_path, "../", "files")
    
        ddf2 = (
            dask.datasets.timeseries(start="2000-01-01", end="2000-01-03", freq="1h")
            .reset_index()
            .map_partitions(lambda x: x.loc[:5])
        )
>       ddf2 = ddf2.set_index("x").reset_index().persist()

dask/dataframe/io/tests/test_parquet.py:2013: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:457: in persist
    out = self.optimize(fuse=fuse)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [timestamp, name, id, x, y]
Index: []
key = '_partitions', value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_apply_infer_columns ___________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_apply_infer_columns():
        df = pd.DataFrame({"x": [1, 2, 3, 4], "y": [10, 20, 30, 40]})
        ddf = from_pandas(df, npartitions=2)
    
        def return_df(x):
            return pd.Series([x.sum(), x.mean()], index=["sum", "mean"])
    
        with pytest.warns(UserWarning, match="metadata"):
            result = ddf.apply(return_df, axis=1)
        assert_eq(result.columns, pd.Index(["sum", "mean"]))
>       assert_eq(result, df.apply(return_df, axis=1))

dask/dataframe/dask_expr/tests/test_collection.py:1530: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   sum     mean
npartitions=2                  
0              float64  floa...=Apply(frame=df, function=<function test_apply_infer_columns.<locals>.return_df at 0x7ffb6a278250>, kwargs={'axis': 1})
b =     sum  mean
0  11.0   5.5
1  22.0  11.0
2  33.0  16.5
3  44.0  22.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   sum     mean
npartitions=2                  
0              float64  floa...=Apply(frame=df, function=<function test_apply_infer_columns.<locals>.return_df at 0x7ffb6a278250>, kwargs={'axis': 1})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_apply_infer_columns.<locals>.return_df at 0x7ffb6a278250>
E                           when serializing tuple item 1
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('apply-9e2b68b7eabf9b3f8a9371bebd0e930d', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
____________ test_filter_nonpartition_columns[pyarrow-pyarrow-None] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = '/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_filter_nonpartition_colum0'
write_engine = 'pyarrow', read_engine = 'pyarrow', calculate_divisions = None

    @pytest.mark.parametrize("calculate_divisions", [None, True])
    def test_filter_nonpartition_columns(
        tmpdir, write_engine, read_engine, calculate_divisions
    ):
        tmpdir = str(tmpdir)
        df_write = pd.DataFrame(
            {
                "id": [1, 2, 3, 4] * 4,
                "time": np.arange(16),
                "random": np.random.choice(["cat", "dog"], size=16),
            }
        )
        ddf_write = dd.from_pandas(df_write, npartitions=4)
        ddf_write.to_parquet(
            tmpdir, write_index=False, partition_on=["id"], engine=write_engine
        )
        ddf_read = dd.read_parquet(
            tmpdir,
            index=False,
            engine=read_engine,
            calculate_divisions=calculate_divisions,
            filters=[(("time", "<", 5))],
        )
>       df_read = ddf_read.compute()

dask/dataframe/io/tests/test_parquet.py:2548: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    time random  id
0     3    dog   4, key = 'id'
value = 0    4
dtype: category
Categories (4, int32): [1, 2, 3, 4]

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_filter_nonpartition_columns[pyarrow-pyarrow-True] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = '/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_filter_nonpartition_colum1'
write_engine = 'pyarrow', read_engine = 'pyarrow', calculate_divisions = True

    @pytest.mark.parametrize("calculate_divisions", [None, True])
    def test_filter_nonpartition_columns(
        tmpdir, write_engine, read_engine, calculate_divisions
    ):
        tmpdir = str(tmpdir)
        df_write = pd.DataFrame(
            {
                "id": [1, 2, 3, 4] * 4,
                "time": np.arange(16),
                "random": np.random.choice(["cat", "dog"], size=16),
            }
        )
        ddf_write = dd.from_pandas(df_write, npartitions=4)
        ddf_write.to_parquet(
            tmpdir, write_index=False, partition_on=["id"], engine=write_engine
        )
        ddf_read = dd.read_parquet(
            tmpdir,
            index=False,
            engine=read_engine,
            calculate_divisions=calculate_divisions,
            filters=[(("time", "<", 5))],
        )
>       df_read = ddf_read.compute()

dask/dataframe/io/tests/test_parquet.py:2548: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    time random  id
0     2    dog   3, key = 'id'
value = 0    3
dtype: category
Categories (4, int32): [1, 2, 3, 4]

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_partitioned_no_pandas_metadata[pyarrow-write_cols0] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_partitioned_no_pandas_met0')
engine = 'pyarrow', write_cols = ['col']

    @PYARROW_MARK
    @pytest.mark.parametrize(
        "write_cols",
        [["col"], ["part", "col"]],
    )
    def test_partitioned_no_pandas_metadata(tmpdir, engine, write_cols):
        # See: https://github.com/dask/dask/issues/8087
    
        # Manually construct directory-partitioned dataset
        path1 = tmpdir.mkdir("part=a")
        path2 = tmpdir.mkdir("part=b")
        path1 = os.path.join(path1, "data.parquet")
        path2 = os.path.join(path2, "data.parquet")
    
        # Write partitions without parquet metadata.
        # Note that we always use pyarrow to do this
        # (regardless of the `engine`)
        _df1 = pd.DataFrame({"part": "a", "col": range(5)})
        _df2 = pd.DataFrame({"part": "b", "col": range(5)})
        t1 = pa.Table.from_pandas(
            _df1[write_cols],
            preserve_index=False,
        ).replace_schema_metadata(metadata={})
        pq.write_table(t1, path1)
        t2 = pa.Table.from_pandas(
            _df2[write_cols],
            preserve_index=False,
        ).replace_schema_metadata(metadata={})
        pq.write_table(t2, path2)
    
        # Check results
        expect = pd.concat([_df1, _df2], ignore_index=True)
        result = dd.read_parquet(str(tmpdir), engine=engine)
>       result["part"] = result["part"].astype("object")

dask/dataframe/io/tests/test_parquet.py:2732: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [col, part]
Index: [], key = 'part'
value = Series([], Name: part, dtype: object)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_partitioned_no_pandas_metadata[pyarrow-write_cols1] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_partitioned_no_pandas_met1')
engine = 'pyarrow', write_cols = ['part', 'col']

    @PYARROW_MARK
    @pytest.mark.parametrize(
        "write_cols",
        [["col"], ["part", "col"]],
    )
    def test_partitioned_no_pandas_metadata(tmpdir, engine, write_cols):
        # See: https://github.com/dask/dask/issues/8087
    
        # Manually construct directory-partitioned dataset
        path1 = tmpdir.mkdir("part=a")
        path2 = tmpdir.mkdir("part=b")
        path1 = os.path.join(path1, "data.parquet")
        path2 = os.path.join(path2, "data.parquet")
    
        # Write partitions without parquet metadata.
        # Note that we always use pyarrow to do this
        # (regardless of the `engine`)
        _df1 = pd.DataFrame({"part": "a", "col": range(5)})
        _df2 = pd.DataFrame({"part": "b", "col": range(5)})
        t1 = pa.Table.from_pandas(
            _df1[write_cols],
            preserve_index=False,
        ).replace_schema_metadata(metadata={})
        pq.write_table(t1, path1)
        t2 = pa.Table.from_pandas(
            _df2[write_cols],
            preserve_index=False,
        ).replace_schema_metadata(metadata={})
        pq.write_table(t2, path2)
    
        # Check results
        expect = pd.concat([_df1, _df2], ignore_index=True)
        result = dd.read_parquet(str(tmpdir), engine=engine)
>       result["part"] = result["part"].astype("object")

dask/dataframe/io/tests/test_parquet.py:2732: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [part, col]
Index: [], key = 'part'
value = Series([], Name: part, dtype: object)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_pyarrow_directory_partitioning ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_pyarrow_directory_partiti0')

    @PYARROW_MARK
    def test_pyarrow_directory_partitioning(tmpdir):
        # Manually construct directory-partitioned dataset
        path1 = tmpdir.mkdir("a")
        path2 = tmpdir.mkdir("b")
        path1 = os.path.join(path1, "data.parquet")
        path2 = os.path.join(path2, "data.parquet")
        _df1 = pd.DataFrame({"part": "a", "col": range(5)})
        _df2 = pd.DataFrame({"part": "b", "col": range(5)})
        _df1.to_parquet(path1)
        _df2.to_parquet(path2)
    
        # Check results
        expect = pd.concat([_df1, _df2], ignore_index=True)
        result = dd.read_parquet(
            str(tmpdir),
            dataset={"partitioning": ["part"], "partition_base_dir": str(tmpdir)},
        )
>       result["part"] = result["part"].astype("object")

dask/dataframe/io/tests/test_parquet.py:2754: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [part, col]
Index: [], key = 'part'
value = Series([], Name: part, dtype: object)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_pyarrow_dataset_partitioned[pyarrow-True] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_pyarrow_dataset_partition0')
engine = 'pyarrow', test_filter = True

    @PYARROW_MARK
    @pytest.mark.parametrize("test_filter", [True, False])
    def test_pyarrow_dataset_partitioned(tmpdir, engine, test_filter):
        fn = str(tmpdir)
        df = pd.DataFrame({"a": [4, 5, 6], "b": ["a", "b", "b"]})
>       df["b"] = df["b"].astype("category")

dask/dataframe/io/tests/test_parquet.py:2858: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b
0  4  a
1  5  b
2  6  b, key = 'b'
value = 0    a
1    b
2    b
Name: b, dtype: category
Categories (2, object): ['a', 'b']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_pyarrow_dataset_partitioned[pyarrow-False] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_pyarrow_dataset_partition1')
engine = 'pyarrow', test_filter = False

    @PYARROW_MARK
    @pytest.mark.parametrize("test_filter", [True, False])
    def test_pyarrow_dataset_partitioned(tmpdir, engine, test_filter):
        fn = str(tmpdir)
        df = pd.DataFrame({"a": [4, 5, 6], "b": ["a", "b", "b"]})
>       df["b"] = df["b"].astype("category")

dask/dataframe/io/tests/test_parquet.py:2858: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b
0  4  a
1  5  b
2  6  b, key = 'b'
value = 0    a
1    b
2    b
Name: b, dtype: category
Categories (2, object): ['a', 'b']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_pyarrow_dataset_read_from_paths _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_pyarrow_dataset_read_from0')

    @PYARROW_MARK
    def test_pyarrow_dataset_read_from_paths(tmpdir):
        fn = str(tmpdir)
        df = pd.DataFrame({"a": [4, 5, 6], "b": ["a", "b", "b"]})
>       df["b"] = df["b"].astype("category")

dask/dataframe/io/tests/test_parquet.py:2916: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b
0  4  a
1  5  b
2  6  b, key = 'b'
value = 0    a
1    b
2    b
Name: b, dtype: category
Categories (2, object): ['a', 'b']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_pyarrow_dataset_filter_partitioned[True] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_pyarrow_dataset_filter_pa0')
split_row_groups = True

    @PYARROW_MARK
    @pytest.mark.parametrize("split_row_groups", [True, False])
    def test_pyarrow_dataset_filter_partitioned(tmpdir, split_row_groups):
        fn = str(tmpdir)
        df = pd.DataFrame(
            {
                "a": [4, 5, 6],
                "b": ["a", "b", "b"],
                "c": ["A", "B", "B"],
            }
        )
>       df["b"] = df["b"].astype("category")

dask/dataframe/io/tests/test_parquet.py:2939: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c
0  4  a  A
1  5  b  B
2  6  b  B, key = 'b'
value = 0    a
1    b
2    b
Name: b, dtype: category
Categories (2, object): ['a', 'b']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_pyarrow_dataset_filter_partitioned[False] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_pyarrow_dataset_filter_pa1')
split_row_groups = False

    @PYARROW_MARK
    @pytest.mark.parametrize("split_row_groups", [True, False])
    def test_pyarrow_dataset_filter_partitioned(tmpdir, split_row_groups):
        fn = str(tmpdir)
        df = pd.DataFrame(
            {
                "a": [4, 5, 6],
                "b": ["a", "b", "b"],
                "c": ["A", "B", "B"],
            }
        )
>       df["b"] = df["b"].astype("category")

dask/dataframe/io/tests/test_parquet.py:2939: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c
0  4  a  A
1  5  b  B
2  6  b  B, key = 'b'
value = 0    a
1    b
2    b
Name: b, dtype: category
Categories (2, object): ['a', 'b']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_pyarrow_dataset_filter_on_partitioned[pyarrow] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_pyarrow_dataset_filter_on0')
engine = 'pyarrow'

    def test_pyarrow_dataset_filter_on_partitioned(tmpdir, engine):
        # See: https://github.com/dask/dask/issues/9246
        df = pd.DataFrame({"val": range(7), "part": list("abcdefg")})
        ddf = dd.from_map(
            lambda i: df.iloc[i : i + 1],
            range(7),
        )
        ddf.to_parquet(tmpdir, engine=engine, partition_on=["part"])
    
        # Check that List[Tuple] filters are applied
        read_ddf = dd.read_parquet(
            tmpdir,
            engine=engine,
            filters=[("part", "==", "c")],
        )
>       read_ddf["part"] = read_ddf["part"].astype("object")

dask/dataframe/io/tests/test_parquet.py:2971: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [val, part]
Index: [], key = 'part'
value = Series([], Name: part, dtype: object)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_to_parquet_overwrite_files_from_read_parquet_in_same_call_raises[pyarrow] _
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_to_parquet_overwrite_file0')
engine = 'pyarrow'

    @pytest.mark.xfail(
        sys.platform == "win32",
        reason="File not found error on windows",
    )
    def test_to_parquet_overwrite_files_from_read_parquet_in_same_call_raises(
        tmpdir, engine
    ):
        subdir = tmpdir.mkdir("subdir")
    
        dd.from_pandas(pd.DataFrame({"x": range(20)}), npartitions=2).to_parquet(
            subdir, engine=engine
        )
    
        ddf = dd.read_parquet(subdir)
    
        # Test writing to the same files, as well as a parent directory
        for target in [subdir, tmpdir]:
            with pytest.raises(
                ValueError, match="same parquet file|Cannot overwrite a path"
            ):
                ddf.to_parquet(target, overwrite=True)
    
>           ddf2 = ddf.assign(y=ddf.x + 1)

dask/dataframe/io/tests/test_parquet.py:3236: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'y'
value = Series([], Name: x, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________________ test_avoid_alignment _____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_avoid_alignment():
        a = pd.DataFrame({"x": range(100)})
        da = from_pandas(a, npartitions=4)
    
        b = pd.DataFrame({"y": range(100)})
>       b["z"] = b.y * 2

dask/dataframe/dask_expr/tests/test_collection.py:2102: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      y
0    0
1    1
2    2
3    3
4    4
..  ..
95  95
96  96
97  97
98  98
99  99

[100 rows x 1 columns]
key = 'z'
value = 0       0
1       2
2       4
3       6
4       8
     ... 
95    190
96    192
97    194
98    196
99    198
Name: y, Length: 100, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_dir_filter[pyarrow] ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_dir_filter_pyarrow_0')
engine = 'pyarrow'

    def test_dir_filter(tmpdir, engine):
        # github #6898
        df = pd.DataFrame.from_dict(
            {
                "A": {
                    0: 351.0,
                    1: 355.0,
                    2: 358.0,
                    3: 266.0,
                    4: 266.0,
                    5: 268.0,
                    6: np.nan,
                },
                "B": {
                    0: 2063.0,
                    1: 2051.0,
                    2: 1749.0,
                    3: 4281.0,
                    4: 3526.0,
                    5: 3462.0,
                    6: np.nan,
                },
                "year": {0: 2019, 1: 2019, 2: 2020, 3: 2020, 4: 2020, 5: 2020, 6: 2020},
            }
        )
        ddf = dask.dataframe.from_pandas(df, npartitions=1)
        ddf.to_parquet(tmpdir, partition_on="year", engine=engine)
        ddf2 = dd.read_parquet(tmpdir, filters=[("year", "==", 2020)], engine=engine)
>       ddf2["year"] = ddf2.year.astype("int64")

dask/dataframe/io/tests/test_parquet.py:3279: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B, year]
Index: [], key = 'year'
value = Series([], Name: year, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_extra_file[pyarrow-None] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

args = ('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_extra_file_pyarrow_None_0',)
kwargs = {'calculate_divisions': True, 'engine': 'pyarrow'}
func = <function read_parquet at 0x7f5bf9b685c0>
exc = FutureWarning('An error occurred while calling the read_parquet method registered to the pandas backend.\nOriginal Mes...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
>           return func(*args, **kwargs)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:140: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5372: in read_parquet
    ReadParquetFSSpec(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:844: in __new__
    inst = super().__new__(cls, *args, _determ_token=_determ_token, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:72: in __new__
    inst._name
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:790: in _name
    return self._funcname + "-" + self.deterministic_token
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:517: in deterministic_token
    self._determ_token = self.__dask_tokenize__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:784: in __dask_tokenize__
    funcname(type(self)), self.checksum, *self.operands[:-1]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:794: in checksum
    return self._dataset_info["checksum"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/io/parquet.py:1398: in _dataset_info
    meta = self.engine._create_dd_meta(dataset_info)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:1211: in _create_dd_meta
    meta = clear_known_categories(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:250: in clear_known_categories
    x[c] = x[c].cat.set_categories([UNKNOWN_CATEGORIES])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = 'b'
value = Series([], Name: b, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning

The above exception was the direct cause of the following exception:

tmpdir = '/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_extra_file_pyarrow_None_0'
engine = 'pyarrow', partition_on = None

    @PYARROW_MARK
    @pytest.mark.parametrize("partition_on", ("b", None))
    def test_extra_file(tmpdir, engine, partition_on):
        # Check that read_parquet can handle spark output
        # See: https://github.com/dask/dask/issues/8087
        tmpdir = str(tmpdir)
        df = pd.DataFrame({"a": range(100), "b": ["dog", "cat"] * 50})
        df = df.assign(b=df.b.astype("category"))
        ddf = dd.from_pandas(df, npartitions=2)
        ddf.to_parquet(
            tmpdir,
            engine=engine,
            write_metadata_file=True,
            partition_on=partition_on,
        )
        open(os.path.join(tmpdir, "_SUCCESS"), "w").close()
        open(os.path.join(tmpdir, "part.0.parquet.crc"), "w").close()
        os.remove(os.path.join(tmpdir, "_metadata"))
>       out = dd.read_parquet(tmpdir, engine=engine, calculate_divisions=True)

dask/dataframe/io/tests/test_parquet.py:3491: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = ('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_extra_file_pyarrow_None_0',)
kwargs = {'calculate_divisions': True, 'engine': 'pyarrow'}
func = <function read_parquet at 0x7f5bf9b685c0>
exc = FutureWarning('An error occurred while calling the read_parquet method registered to the pandas backend.\nOriginal Mes...documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n')

    @wraps(fn)
    def wrapper(*args, **kwargs):
        func = getattr(self, dispatch_name)
        try:
            return func(*args, **kwargs)
        except Exception as e:
            try:
                exc = type(e)(
                    f"An error occurred while calling the {funcname(func)} "
                    f"method registered to the {self.backend} backend.\n"
                    f"Original Message: {e}"
                )
            except TypeError:
                raise e
            else:
>               raise exc from e
E               FutureWarning: An error occurred while calling the read_parquet method registered to the pandas backend.
E               Original Message: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

../BUILDROOT/usr/lib/python3.14/site-packages/dask/backends.py:151: FutureWarning
____________________ test_replace_filtered_combine_similar _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_replace_filtered_combine_similar():
        from dask.dataframe.dask_expr._expr import Filter, Replace
    
        pdf = pd.DataFrame({"a": [1, 2, 3, 4, 5, 6], "b": 1, "c": 2})
    
        df = from_pandas(pdf.copy(), npartitions=2)
        df = df[df.a > 3]
        df = df.replace(5, 4)
>       df["new"] = df.a + df.b

dask/dataframe/dask_expr/tests/test_collection.py:2253: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b, c]
Index: [], key = 'new'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_custom_filename_with_partition[pyarrow] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_custom_filename_with_part0')
engine = 'pyarrow'

    def test_custom_filename_with_partition(tmpdir, engine):
        fn = str(tmpdir)
        pdf = pd.DataFrame(
            {
                "first_name": ["frank", "li", "marcela", "luis"],
                "country": ["canada", "china", "venezuela", "venezuela"],
            },
        )
        df = dd.from_pandas(pdf, npartitions=4)
        df.to_parquet(
            fn,
            engine=engine,
            partition_on=["country"],
            name_function=lambda x: f"{x}-cool.parquet",
            write_index=False,
        )
    
        for _, dirs, files in os.walk(fn):
            for dir in dirs:
                assert dir in (
                    "country=canada",
                    "country=china",
                    "country=venezuela",
                )
            for file in files:
                assert file in (
                    *[f"{i}-cool.parquet" for i in range(df.npartitions)],
                    "_common_metadata",
                    "_metadata",
                )
        actual = dd.read_parquet(fn, engine=engine, index=False)
>       assert_eq(
            pdf, actual, check_index=False, check_dtype=False, check_categorical=False
        )

dask/dataframe/io/tests/test_parquet.py:3666: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:548: in assert_eq
    b = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   first_name    country
0       luis  venezuela, key = 'country'
value = 0    venezuela
dtype: category
Categories (3, object): ['canada', 'china', 'venezuela']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_roundtrip_partitioned_pyarrow_dataset[pyarrow] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmpdir = local('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_roundtrip_partitioned_pya0')
engine = 'pyarrow'

    @PYARROW_MARK
    def test_roundtrip_partitioned_pyarrow_dataset(tmpdir, engine):
        # See: https://github.com/dask/dask/issues/8650
    
        import pyarrow.parquet as pq
        from pyarrow.dataset import HivePartitioning, write_dataset
    
        # Sample data
        df = pd.DataFrame({"col1": [1, 2], "col2": ["a", "b"]})
    
        # Write partitioned dataset with dask
        dask_path = tmpdir.mkdir("foo-dask")
        ddf = dd.from_pandas(df, npartitions=2)
        ddf.to_parquet(dask_path, engine=engine, partition_on=["col1"], write_index=False)
    
        # Write partitioned dataset with pyarrow
        pa_path = tmpdir.mkdir("foo-pyarrow")
        table = pa.Table.from_pandas(df)
        write_dataset(
            data=table,
            base_dir=pa_path,
            basename_template="part.{i}.parquet",
            format="parquet",
            partitioning=HivePartitioning(pa.schema([("col1", pa.int32())])),
        )
    
        # Define simple function to ensure results should
        # be comparable (same column and row order)
        def _prep(x):
            return x.sort_values("col2")[["col1", "col2"]]
    
        # Check that reading dask-written data is the same for pyarrow and dask
        df_read_dask = dd.read_parquet(dask_path, engine=engine)
        df_read_pa = pq.read_table(dask_path).to_pandas()
>       assert_eq(_prep(df_read_dask), _prep(df_read_pa), check_index=False)

dask/dataframe/io/tests/test_parquet.py:3705: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =   col2  col1
0    b     2, key = 'col1'
value = 0    2
dtype: category
Categories (2, int32): [1, 2]

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________________ test_combine _________________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_combine():
        df1 = pd.DataFrame(
            {
                "A": np.random.choice([1, 2, np.nan], 100),
                "B": np.random.choice(["a", "b", "nan"], 100),
            }
        )
    
        df2 = pd.DataFrame(
            {
                "A": np.random.choice([1, 2, 3], 100),
                "B": np.random.choice(["a", "b", "c"], 100),
            }
        )
        ddf1 = from_pandas(df1, 4)
        ddf2 = from_pandas(df2, 5)
    
        first = lambda a, b: a
    
        # You can add series with strings and nans but you can't add scalars 'a' + np.nan
        str_add = lambda a, b: a + b if a is not np.nan else a
    
        # DataFrame
        for dda, ddb, a, b, runs in [
            # (ddf1, ddf2, df1, df2, [(add, None), (first, None)]),
            (ddf1.A, ddf2.A, df1.A, df2.A, [(add, None), (add, 100), (first, None)]),
            (
                ddf1.B,
                ddf2.B,
                df1.B,
                df2.B,
                [(str_add, None), (str_add, "d"), (first, None)],
            ),
        ]:
            for func, fill_value in runs:
                sol = a.combine(b, func, fill_value=fill_value)
>               assert_eq(dda.combine(ddb, func, fill_value=fill_value), sol)

dask/dataframe/dask_expr/tests/test_collection.py:2326: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask Series Structure:
npartitions=8
0     float64
20        ...
       ...   
80        ...
99        ...
Dask Name: ...Expr=CombineSeriesAlign(frame=df['A'], other=df['A'], func=<function test_combine.<locals>.<lambda> at 0x7ffb848e4670>)
b = 0     2.0
1     NaN
2     2.0
3     NaN
4     1.0
     ... 
95    1.0
96    2.0
97    NaN
98    1.0
99    2.0
Name: A, Length: 100, dtype: float64
serialize_graph = True, args = (), kwargs = {}
obj = Dask Series Structure:
npartitions=8
0     float64
20        ...
       ...   
80        ...
99        ...
Dask Name: ...Expr=CombineSeriesAlign(frame=df['A'], other=df['A'], func=<function test_combine.<locals>.<lambda> at 0x7ffb848e4670>)

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_combine.<locals>.<lambda> at 0x7ffb848e4670>
E                           when serializing tuple item 2
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('combine-7cb0609c45ee49ab5361d2c98b88db1d', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________ test_read_parquet_preserve_categorical_column_dtype ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

tmp_path = PosixPath('/tmp/pytest-of-mockbuild/pytest-0/popen-gw0/test_read_parquet_preserve_cat0')

    @PYARROW_MARK
    def test_read_parquet_preserve_categorical_column_dtype(tmp_path):
        df = pd.DataFrame({"a": [1, 2], "b": ["x", "y"]})
    
        outdir = tmp_path / "out.parquet"
        df.to_parquet(outdir, partition_cols=["a"])
        ddf = dd.read_parquet(outdir)
    
        expected = pd.DataFrame(
            {"b": ["x", "y"], "a": pd.Categorical(pd.Index([1, 2], dtype="int32"))},
            index=[0, 0],
        )
>       assert_eq(ddf, expected)

dask/dataframe/io/tests/test_parquet.py:3989: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:545: in assert_eq
    a = _check_dask(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:435: in _check_dask
    result = dsk.compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:82: in __call__
    return read_parquet_part(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/core.py:184: in read_parquet_part
    func(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/parquet/arrow.py:602: in read_partition
    df[partition.name] = pd.Series(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    b  a
0  y  2, key = 'a'
value = 0    2
dtype: category
Categories (2, int32): [1, 2]

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_query_index_from_query __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

db = 'sqlite:////tmp/tmpn_xujt6u'

    def test_query_index_from_query(db):
        from sqlalchemy import sql
    
        number = sql.column("number")
        name = sql.column("name")
        s1 = sql.select(number, name, sql.func.length(name).label("lenname")).select_from(
            sql.table("test")
        )
        out = read_sql_query(s1, db, npartitions=2, index_col="lenname")
    
        lenname_df = df.copy()
>       lenname_df["lenname"] = lenname_df["name"].str.len()

dask/dataframe/io/tests/test_sql.py:427: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =            name  age  negish
number                      
0         Alice   33      -5
1           Bob   40      -3
2 ...  3
3          Dora   16       5
4         Edith   53       0
5       Francis   30       0
6       Garreth   20       0
key = 'lenname'
value = number
0    5
1    3
2    5
3    4
4    5
5    7
6    7
Name: name, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________________ test_to_sql[False-2] _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 2, parallel = False

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("parallel", (False, True))
    def test_to_sql(npartitions, parallel):
        df_by_age = df.set_index("age")
        df_appended = pd.concat(
            [
                df,
                df,
            ]
        )
    
        ddf = dd.from_pandas(df, npartitions)
        ddf_by_age = ddf.set_index("age")
    
        # Simple round trip test: use existing "number" index_col
        with tmp_db_uri() as uri:
            ddf.to_sql("test", uri, parallel=parallel)
            result = read_sql_table("test", uri, "number")
            assert_eq(df, result)
    
        # Test writing no index, and reading back in with one of the other columns as index (`read_sql_table` requires
        # an index_col)
        with tmp_db_uri() as uri:
            ddf.to_sql("test", uri, parallel=parallel, index=False)
    
            result = read_sql_table("test", uri, "negish")
            assert_eq(df.set_index("negish"), result)
    
            result = read_sql_table("test", uri, "age")
            assert_eq(df_by_age, result)
    
        # Index by "age" instead
        with tmp_db_uri() as uri:
>           ddf_by_age.to_sql("test", uri, parallel=parallel)

dask/dataframe/io/tests/test_sql.py:510: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2409: in to_sql
    return to_sql(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/sql.py:588: in to_sql
    for d in df.to_delayed():
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2506: in to_delayed
    frame = self.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [name, age, negish]
Index: []
key = '_partitions', value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________________ test_to_sql[True-2] ______________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 2, parallel = True

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("parallel", (False, True))
    def test_to_sql(npartitions, parallel):
        df_by_age = df.set_index("age")
        df_appended = pd.concat(
            [
                df,
                df,
            ]
        )
    
        ddf = dd.from_pandas(df, npartitions)
        ddf_by_age = ddf.set_index("age")
    
        # Simple round trip test: use existing "number" index_col
        with tmp_db_uri() as uri:
            ddf.to_sql("test", uri, parallel=parallel)
            result = read_sql_table("test", uri, "number")
            assert_eq(df, result)
    
        # Test writing no index, and reading back in with one of the other columns as index (`read_sql_table` requires
        # an index_col)
        with tmp_db_uri() as uri:
            ddf.to_sql("test", uri, parallel=parallel, index=False)
    
            result = read_sql_table("test", uri, "negish")
            assert_eq(df.set_index("negish"), result)
    
            result = read_sql_table("test", uri, "age")
            assert_eq(df_by_age, result)
    
        # Index by "age" instead
        with tmp_db_uri() as uri:
>           ddf_by_age.to_sql("test", uri, parallel=parallel)

dask/dataframe/io/tests/test_sql.py:510: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2409: in to_sql
    return to_sql(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/io/sql.py:582: in to_sql
    for d in df.to_delayed()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2506: in to_delayed
    frame = self.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [name, age, negish]
Index: []
key = '_partitions', value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_assign_projection_mix __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_assign_projection_mix():
        data = {
            "date": [
                "2024-03-22 18:13:36.801000",
                "2024-03-22 18:14:11.457000",
                "2024-04-02 06:05:01.658000",
                "2024-04-02 06:05:04.870000",
                "2024-04-03 06:11:30.202000",
            ],
            "code": [1.0, 3.0, 6.0, 6.0, 8.0],
            "first": pd.NaT,
            "first_2": pd.NaT,
            "second": pd.NaT,
            "second_2": pd.NaT,
            "third": pd.NaT,
        }
        df = pd.DataFrame(data)
>       df["date"] = pd.to_datetime(df["date"])

dask/dataframe/dask_expr/tests/test_collection.py:2690: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =                          date  code first first_2 second second_2 third
0  2024-03-22 18:13:36.801000   1.0   NaT     ...70000   6.0   NaT     NaT    NaT      NaT   NaT
4  2024-04-03 06:11:30.202000   8.0   NaT     NaT    NaT      NaT   NaT
key = 'date'
value = 0   2024-03-22 18:13:36.801
1   2024-03-22 18:14:11.457
2   2024-04-02 06:05:01.658
3   2024-04-02 06:05:04.870
4   2024-04-03 06:11:30.202
Name: date, dtype: datetime64[ns]

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_empty_from_pandas_projection _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_empty_from_pandas_projection():
        pdf = pd.DataFrame({"x": [1, 2, 3]})
        df = from_pandas(pdf, npartitions=2)
        foo = pd.Series(["a"] * 20, dtype="category")
>       df["foo"] = from_pandas(foo, npartitions=1)

dask/dataframe/dask_expr/tests/test_collection.py:2751: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2814: in assign
    result = new_collection(expr.AssignAlign(result, k, v.expr))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:3520: in _meta
    return self._expr_cls(*self.operands)._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'foo'
value = Series([], dtype: category
Categories (1, object): ['a'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_binop_scalar_left ____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_binop_scalar_left():
        pdf = pd.DataFrame(
            {
                "x": [0, 1, 0, 1],
                "y": [0, 0, 1, 1],
                "z": [1, 2, 3, 4],
                "beam_flag": [5, 0, 0, 0],
                "ping_number": [0] * 4,
                "beam_number": [0] * 4,
                "filename": [0] * 4,
            }
        )
        df = from_pandas(pdf, npartitions=1)
    
>       df["cell_x"] = ((df.x - df.x.min()) // 1).astype("uint32")

dask/dataframe/dask_expr/tests/test_collection.py:2770: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z, beam_flag, ping_number, beam_number, filename]
Index: []
key = 'cell_x', value = Series([], Name: x, dtype: uint32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_align_known_divisions_in_assign _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_align_known_divisions_in_assign():
        df = pd.DataFrame({"a": [1, 2], 1: [5, 6]})
        ddf = from_pandas(df, npartitions=2)
>       ddf = ddf.assign(c=df["a"])

dask/dataframe/dask_expr/tests/test_collection.py:2814: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2814: in assign
    result = new_collection(expr.AssignAlign(result, k, v.expr))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:3520: in _meta
    return self._expr_cls(*self.operands)._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, 1]
Index: [], key = 'c'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_concat_ignore_order ___________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_concat_ignore_order():
        pdf1 = pd.DataFrame(
            {
                "x": pd.Categorical(
                    ["a", "b", "c", "a"], categories=["a", "b", "c"], ordered=True
                )
            }
        )
        ddf1 = from_pandas(pdf1, 2)
        pdf2 = pd.DataFrame(
            {"x": pd.Categorical(["c", "b", "a"], categories=["c", "b", "a"], ordered=True)}
        )
        ddf2 = from_pandas(pdf2, 2)
        expected = pd.concat([pdf1, pdf2])
>       expected["x"] = expected["x"].astype("category")

dask/dataframe/dask_expr/tests/test_concat.py:158: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x
0  a
1  b
2  c
3  a
0  c
1  b
2  a, key = 'x'
value = 0    a
1    b
2    c
3    a
0    c
1    b
2    a
Name: x, dtype: category
Categories (3, object): ['a', 'b', 'c']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_optimize_fusion_many ___________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_optimize_fusion_many():
        # Test that many `Blockwise`` operations,
        # originating from various IO operations,
        # can all be fused together
        a = from_pandas(pd.DataFrame({"x": range(100), "y": range(100)}), 10)
        b = from_pandas(pd.DataFrame({"a": range(100)}), 10)
    
        # some generic elemwise operations
        aa = a[["x"]] + 1
>       aa["a"] = a["y"] + a["x"]

dask/dataframe/dask_expr/tests/test_fusion.py:59: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'a'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_groupby_reduction_optimize ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

pdf =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]
df = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
0              int64  ... ...
99               ...    ...    ...
Dask Name: replace, 2 expressions
Expr=Replace(frame=df, to_replace=1, value=5)

    def test_groupby_reduction_optimize(pdf, df):
        df = df.replace(1, 5)
        agg = df.groupby(df.x).y.sum()
        expected_query = df[["x", "y"]]
        expected_query = expected_query.groupby(expected_query.x).y.sum()
        assert agg.optimize()._name == expected_query.optimize()._name
        expect = pdf.replace(1, 5).groupby(["x"]).y.sum()
        assert_eq(agg, expect)
    
        df2 = df[["y"]]
        agg = df2.groupby(df.x).y.sum()
        ops = [
            op for op in agg.expr.optimize(fuse=False).walk() if isinstance(op, FromPandas)
        ]
        assert len(ops) == 1
        assert ops[0].columns == ["x", "y"]
    
        df2 = df[["y"]]
        with pytest.warns(UserWarning, match="inferred from partial data"):
            agg = df2.groupby(df.x).y.apply(lambda x: x)
        ops = [
>           op for op in agg.expr.optimize(fuse=False).walk() if isinstance(op, FromPandas)
        ]

dask/dataframe/dask_expr/tests/test_groupby.py:120: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_0'
value = Series([], Name: x, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_datetime_std_creates_copy_cols[False-0] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 0, numeric_only = False

    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_creates_copy_cols(axis, numeric_only):
        pdf = pd.DataFrame(
            {
                "dt1": [
                    datetime.fromtimestamp(1636426700 + (i * 250000)) for i in range(10)
                ],
                "dt2": [
                    datetime.fromtimestamp(1636426700 + (i * 300000)) for i in range(10)
                ],
            }
        )
    
        ddf = dd.from_pandas(pdf, 3)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
    
        # Series test (same line twice to make sure data structure wasn't mutated)
        assert_eq(ddf["dt1"].std(**kwargs), pdf["dt1"].std(**kwargs))
        assert_eq(ddf["dt1"].std(**kwargs), pdf["dt1"].std(**kwargs))
    
        # DataFrame test (same line twice to make sure data structure wasn't mutated)
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs)

dask/dataframe/tests/test_arithmetics_reduction.py:1313: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2]
Index: [], key = 'dt1'
value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_datetime_std_creates_copy_cols[False-1] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 1, numeric_only = False

    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_creates_copy_cols(axis, numeric_only):
        pdf = pd.DataFrame(
            {
                "dt1": [
                    datetime.fromtimestamp(1636426700 + (i * 250000)) for i in range(10)
                ],
                "dt2": [
                    datetime.fromtimestamp(1636426700 + (i * 300000)) for i in range(10)
                ],
            }
        )
    
        ddf = dd.from_pandas(pdf, 3)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
    
        # Series test (same line twice to make sure data structure wasn't mutated)
        assert_eq(ddf["dt1"].std(**kwargs), pdf["dt1"].std(**kwargs))
        assert_eq(ddf["dt1"].std(**kwargs), pdf["dt1"].std(**kwargs))
    
        # DataFrame test (same line twice to make sure data structure wasn't mutated)
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs)

dask/dataframe/tests/test_arithmetics_reduction.py:1313: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2]
Index: [], key = 'dt1'
value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_groupby_group_keys[True] _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

group_keys = True
pdf =      y  z
x        
0    0  1
1    1  1
2    2  1
3    3  1
4    4  1
..  .. ..
5   95  1
6   96  1
7   97  1
8   98  1
9   99  1

[100 rows x 2 columns]

    @pytest.mark.parametrize("group_keys", [True, False, None])
    def test_groupby_group_keys(group_keys, pdf):
        pdf = pdf.set_index("x")
        df = from_pandas(pdf, npartitions=10)
    
        func = lambda g: g.copy()
        expected = pdf.groupby("x").apply(func)
>       assert_eq(expected, df.groupby("x").apply(func, meta=expected))

dask/dataframe/dask_expr/tests/test_groupby.py:245: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a =       y  z
x x       
0 0   0  1
  0  10  1
  0  20  1
  0  30  1
  0  40  1
..   .. ..
9 9  59  1
  9  69  1
  9  79  1
  9  89  1
  9  99  1

[100 rows x 2 columns]
b = Dask DataFrame Structure:
                    y      z
npartitions=10              
0               int64  int64
1    ...me=df, observed=False, func=<function test_groupby_group_keys.<locals>.<lambda> at 0x7ffb849d0720>, args=(), kwargs={})
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                    y      z
npartitions=10              
0               int64  int64
1    ...me=df, observed=False, func=<function test_groupby_group_keys.<locals>.<lambda> at 0x7ffb849d0720>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_groupby_group_keys.<locals>.<lambda> at 0x7ffb849d0720>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-19b909e9ac54b797d3e7f3292668381a', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
________________________ test_groupby_group_keys[False] ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

group_keys = False
pdf =      y  z
x        
0    0  1
1    1  1
2    2  1
3    3  1
4    4  1
..  .. ..
5   95  1
6   96  1
7   97  1
8   98  1
9   99  1

[100 rows x 2 columns]

    @pytest.mark.parametrize("group_keys", [True, False, None])
    def test_groupby_group_keys(group_keys, pdf):
        pdf = pdf.set_index("x")
        df = from_pandas(pdf, npartitions=10)
    
        func = lambda g: g.copy()
        expected = pdf.groupby("x").apply(func)
>       assert_eq(expected, df.groupby("x").apply(func, meta=expected))

dask/dataframe/dask_expr/tests/test_groupby.py:245: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a =       y  z
x x       
0 0   0  1
  0  10  1
  0  20  1
  0  30  1
  0  40  1
..   .. ..
9 9  59  1
  9  69  1
  9  79  1
  9  89  1
  9  99  1

[100 rows x 2 columns]
b = Dask DataFrame Structure:
                    y      z
npartitions=10              
0               int64  int64
1    ...me=df, observed=False, func=<function test_groupby_group_keys.<locals>.<lambda> at 0x7ffb849d0720>, args=(), kwargs={})
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                    y      z
npartitions=10              
0               int64  int64
1    ...me=df, observed=False, func=<function test_groupby_group_keys.<locals>.<lambda> at 0x7ffb849d0720>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_groupby_group_keys.<locals>.<lambda> at 0x7ffb849d0720>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-19b909e9ac54b797d3e7f3292668381a', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_________________ test_datetime_std_creates_copy_cols[None-0] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 0, numeric_only = None

    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_creates_copy_cols(axis, numeric_only):
        pdf = pd.DataFrame(
            {
                "dt1": [
                    datetime.fromtimestamp(1636426700 + (i * 250000)) for i in range(10)
                ],
                "dt2": [
                    datetime.fromtimestamp(1636426700 + (i * 300000)) for i in range(10)
                ],
            }
        )
    
        ddf = dd.from_pandas(pdf, 3)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
    
        # Series test (same line twice to make sure data structure wasn't mutated)
        assert_eq(ddf["dt1"].std(**kwargs), pdf["dt1"].std(**kwargs))
        assert_eq(ddf["dt1"].std(**kwargs), pdf["dt1"].std(**kwargs))
    
        # DataFrame test (same line twice to make sure data structure wasn't mutated)
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs)

dask/dataframe/tests/test_arithmetics_reduction.py:1313: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2]
Index: [], key = 'dt1'
value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_groupby_group_keys[None] _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

group_keys = None
pdf =      y  z
x        
0    0  1
1    1  1
2    2  1
3    3  1
4    4  1
..  .. ..
5   95  1
6   96  1
7   97  1
8   98  1
9   99  1

[100 rows x 2 columns]

    @pytest.mark.parametrize("group_keys", [True, False, None])
    def test_groupby_group_keys(group_keys, pdf):
        pdf = pdf.set_index("x")
        df = from_pandas(pdf, npartitions=10)
    
        func = lambda g: g.copy()
        expected = pdf.groupby("x").apply(func)
>       assert_eq(expected, df.groupby("x").apply(func, meta=expected))

dask/dataframe/dask_expr/tests/test_groupby.py:245: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a =       y  z
x x       
0 0   0  1
  0  10  1
  0  20  1
  0  30  1
  0  40  1
..   .. ..
9 9  59  1
  9  69  1
  9  79  1
  9  89  1
  9  99  1

[100 rows x 2 columns]
b = Dask DataFrame Structure:
                    y      z
npartitions=10              
0               int64  int64
1    ...me=df, observed=False, func=<function test_groupby_group_keys.<locals>.<lambda> at 0x7ffb849d0720>, args=(), kwargs={})
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                    y      z
npartitions=10              
0               int64  int64
1    ...me=df, observed=False, func=<function test_groupby_group_keys.<locals>.<lambda> at 0x7ffb849d0720>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_groupby_group_keys.<locals>.<lambda> at 0x7ffb849d0720>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-19b909e9ac54b797d3e7f3292668381a', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_________________ test_datetime_std_creates_copy_cols[None-1] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 1, numeric_only = None

    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_creates_copy_cols(axis, numeric_only):
        pdf = pd.DataFrame(
            {
                "dt1": [
                    datetime.fromtimestamp(1636426700 + (i * 250000)) for i in range(10)
                ],
                "dt2": [
                    datetime.fromtimestamp(1636426700 + (i * 300000)) for i in range(10)
                ],
            }
        )
    
        ddf = dd.from_pandas(pdf, 3)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
    
        # Series test (same line twice to make sure data structure wasn't mutated)
        assert_eq(ddf["dt1"].std(**kwargs), pdf["dt1"].std(**kwargs))
        assert_eq(ddf["dt1"].std(**kwargs), pdf["dt1"].std(**kwargs))
    
        # DataFrame test (same line twice to make sure data structure wasn't mutated)
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs)

dask/dataframe/tests/test_arithmetics_reduction.py:1313: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2]
Index: [], key = 'dt1'
value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_datetime_std_with_larger_dataset[False-False-0] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 0, skipna = False, numeric_only = False

    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_with_larger_dataset(axis, skipna, numeric_only):
        num_rows = 250
    
        dt1 = pd.concat(
            [
                pd.Series([pd.NaT] * 15, index=range(15)),
                pd.to_datetime(
                    pd.Series(
                        [
                            datetime.fromtimestamp(1636426704 + (i * 250000))
                            for i in range(num_rows - 15)
                        ],
                        index=range(15, 250),
                    )
                ),
            ],
            ignore_index=False,
        )
    
        base_numbers = [
            (1638290040706793300 + (i * 69527182702409)) for i in range(num_rows)
        ]
    
        pdf = pd.DataFrame(
            {"dt1": dt1, "dt2": pd.to_datetime(pd.Series(base_numbers))}, index=range(250)
        )
    
        for i in range(3, 8):
            pdf[f"dt{i}"] = pd.to_datetime(
                pd.Series([int(x + (0.12 * i)) for x in base_numbers])
            )
    
        ddf = dd.from_pandas(pdf, 8)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        # Same thing but as Series. No axis, since axis=1 raises error
        assert_near_timedeltas(ddf["dt1"].std(**kwargs).compute(), pdf["dt1"].std(**kwargs))
    
        # Computation on full dataset
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs).compute()

dask/dataframe/tests/test_arithmetics_reduction.py:1367: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2, dt3, dt4, dt5, dt6, dt7]
Index: []
key = 'dt1', value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_datetime_std_with_larger_dataset[False-False-1] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 1, skipna = False, numeric_only = False

    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_with_larger_dataset(axis, skipna, numeric_only):
        num_rows = 250
    
        dt1 = pd.concat(
            [
                pd.Series([pd.NaT] * 15, index=range(15)),
                pd.to_datetime(
                    pd.Series(
                        [
                            datetime.fromtimestamp(1636426704 + (i * 250000))
                            for i in range(num_rows - 15)
                        ],
                        index=range(15, 250),
                    )
                ),
            ],
            ignore_index=False,
        )
    
        base_numbers = [
            (1638290040706793300 + (i * 69527182702409)) for i in range(num_rows)
        ]
    
        pdf = pd.DataFrame(
            {"dt1": dt1, "dt2": pd.to_datetime(pd.Series(base_numbers))}, index=range(250)
        )
    
        for i in range(3, 8):
            pdf[f"dt{i}"] = pd.to_datetime(
                pd.Series([int(x + (0.12 * i)) for x in base_numbers])
            )
    
        ddf = dd.from_pandas(pdf, 8)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        # Same thing but as Series. No axis, since axis=1 raises error
        assert_near_timedeltas(ddf["dt1"].std(**kwargs).compute(), pdf["dt1"].std(**kwargs))
    
        # Computation on full dataset
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs).compute()

dask/dataframe/tests/test_arithmetics_reduction.py:1367: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2, dt3, dt4, dt5, dt6, dt7]
Index: []
key = 'dt1', value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_datetime_std_with_larger_dataset[False-True-0] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 0, skipna = True, numeric_only = False

    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_with_larger_dataset(axis, skipna, numeric_only):
        num_rows = 250
    
        dt1 = pd.concat(
            [
                pd.Series([pd.NaT] * 15, index=range(15)),
                pd.to_datetime(
                    pd.Series(
                        [
                            datetime.fromtimestamp(1636426704 + (i * 250000))
                            for i in range(num_rows - 15)
                        ],
                        index=range(15, 250),
                    )
                ),
            ],
            ignore_index=False,
        )
    
        base_numbers = [
            (1638290040706793300 + (i * 69527182702409)) for i in range(num_rows)
        ]
    
        pdf = pd.DataFrame(
            {"dt1": dt1, "dt2": pd.to_datetime(pd.Series(base_numbers))}, index=range(250)
        )
    
        for i in range(3, 8):
            pdf[f"dt{i}"] = pd.to_datetime(
                pd.Series([int(x + (0.12 * i)) for x in base_numbers])
            )
    
        ddf = dd.from_pandas(pdf, 8)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        # Same thing but as Series. No axis, since axis=1 raises error
        assert_near_timedeltas(ddf["dt1"].std(**kwargs).compute(), pdf["dt1"].std(**kwargs))
    
        # Computation on full dataset
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs).compute()

dask/dataframe/tests/test_arithmetics_reduction.py:1367: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2, dt3, dt4, dt5, dt6, dt7]
Index: []
key = 'dt1', value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_datetime_std_with_larger_dataset[False-True-1] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 1, skipna = True, numeric_only = False

    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_with_larger_dataset(axis, skipna, numeric_only):
        num_rows = 250
    
        dt1 = pd.concat(
            [
                pd.Series([pd.NaT] * 15, index=range(15)),
                pd.to_datetime(
                    pd.Series(
                        [
                            datetime.fromtimestamp(1636426704 + (i * 250000))
                            for i in range(num_rows - 15)
                        ],
                        index=range(15, 250),
                    )
                ),
            ],
            ignore_index=False,
        )
    
        base_numbers = [
            (1638290040706793300 + (i * 69527182702409)) for i in range(num_rows)
        ]
    
        pdf = pd.DataFrame(
            {"dt1": dt1, "dt2": pd.to_datetime(pd.Series(base_numbers))}, index=range(250)
        )
    
        for i in range(3, 8):
            pdf[f"dt{i}"] = pd.to_datetime(
                pd.Series([int(x + (0.12 * i)) for x in base_numbers])
            )
    
        ddf = dd.from_pandas(pdf, 8)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        # Same thing but as Series. No axis, since axis=1 raises error
        assert_near_timedeltas(ddf["dt1"].std(**kwargs).compute(), pdf["dt1"].std(**kwargs))
    
        # Computation on full dataset
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs).compute()

dask/dataframe/tests/test_arithmetics_reduction.py:1367: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2, dt3, dt4, dt5, dt6, dt7]
Index: []
key = 'dt1', value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_datetime_std_with_larger_dataset[None-False-0] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 0, skipna = False, numeric_only = None

    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_with_larger_dataset(axis, skipna, numeric_only):
        num_rows = 250
    
        dt1 = pd.concat(
            [
                pd.Series([pd.NaT] * 15, index=range(15)),
                pd.to_datetime(
                    pd.Series(
                        [
                            datetime.fromtimestamp(1636426704 + (i * 250000))
                            for i in range(num_rows - 15)
                        ],
                        index=range(15, 250),
                    )
                ),
            ],
            ignore_index=False,
        )
    
        base_numbers = [
            (1638290040706793300 + (i * 69527182702409)) for i in range(num_rows)
        ]
    
        pdf = pd.DataFrame(
            {"dt1": dt1, "dt2": pd.to_datetime(pd.Series(base_numbers))}, index=range(250)
        )
    
        for i in range(3, 8):
            pdf[f"dt{i}"] = pd.to_datetime(
                pd.Series([int(x + (0.12 * i)) for x in base_numbers])
            )
    
        ddf = dd.from_pandas(pdf, 8)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        # Same thing but as Series. No axis, since axis=1 raises error
        assert_near_timedeltas(ddf["dt1"].std(**kwargs).compute(), pdf["dt1"].std(**kwargs))
    
        # Computation on full dataset
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs).compute()

dask/dataframe/tests/test_arithmetics_reduction.py:1367: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2, dt3, dt4, dt5, dt6, dt7]
Index: []
key = 'dt1', value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_datetime_std_with_larger_dataset[None-False-1] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 1, skipna = False, numeric_only = None

    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_with_larger_dataset(axis, skipna, numeric_only):
        num_rows = 250
    
        dt1 = pd.concat(
            [
                pd.Series([pd.NaT] * 15, index=range(15)),
                pd.to_datetime(
                    pd.Series(
                        [
                            datetime.fromtimestamp(1636426704 + (i * 250000))
                            for i in range(num_rows - 15)
                        ],
                        index=range(15, 250),
                    )
                ),
            ],
            ignore_index=False,
        )
    
        base_numbers = [
            (1638290040706793300 + (i * 69527182702409)) for i in range(num_rows)
        ]
    
        pdf = pd.DataFrame(
            {"dt1": dt1, "dt2": pd.to_datetime(pd.Series(base_numbers))}, index=range(250)
        )
    
        for i in range(3, 8):
            pdf[f"dt{i}"] = pd.to_datetime(
                pd.Series([int(x + (0.12 * i)) for x in base_numbers])
            )
    
        ddf = dd.from_pandas(pdf, 8)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        # Same thing but as Series. No axis, since axis=1 raises error
        assert_near_timedeltas(ddf["dt1"].std(**kwargs).compute(), pdf["dt1"].std(**kwargs))
    
        # Computation on full dataset
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs).compute()

dask/dataframe/tests/test_arithmetics_reduction.py:1367: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2, dt3, dt4, dt5, dt6, dt7]
Index: []
key = 'dt1', value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_datetime_std_with_larger_dataset[None-True-0] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 0, skipna = True, numeric_only = None

    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_with_larger_dataset(axis, skipna, numeric_only):
        num_rows = 250
    
        dt1 = pd.concat(
            [
                pd.Series([pd.NaT] * 15, index=range(15)),
                pd.to_datetime(
                    pd.Series(
                        [
                            datetime.fromtimestamp(1636426704 + (i * 250000))
                            for i in range(num_rows - 15)
                        ],
                        index=range(15, 250),
                    )
                ),
            ],
            ignore_index=False,
        )
    
        base_numbers = [
            (1638290040706793300 + (i * 69527182702409)) for i in range(num_rows)
        ]
    
        pdf = pd.DataFrame(
            {"dt1": dt1, "dt2": pd.to_datetime(pd.Series(base_numbers))}, index=range(250)
        )
    
        for i in range(3, 8):
            pdf[f"dt{i}"] = pd.to_datetime(
                pd.Series([int(x + (0.12 * i)) for x in base_numbers])
            )
    
        ddf = dd.from_pandas(pdf, 8)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        # Same thing but as Series. No axis, since axis=1 raises error
        assert_near_timedeltas(ddf["dt1"].std(**kwargs).compute(), pdf["dt1"].std(**kwargs))
    
        # Computation on full dataset
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs).compute()

dask/dataframe/tests/test_arithmetics_reduction.py:1367: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2, dt3, dt4, dt5, dt6, dt7]
Index: []
key = 'dt1', value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_datetime_std_with_larger_dataset[None-True-1] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

axis = 1, skipna = True, numeric_only = None

    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
    @pytest.mark.parametrize("axis", [0, 1])
    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_with_larger_dataset(axis, skipna, numeric_only):
        num_rows = 250
    
        dt1 = pd.concat(
            [
                pd.Series([pd.NaT] * 15, index=range(15)),
                pd.to_datetime(
                    pd.Series(
                        [
                            datetime.fromtimestamp(1636426704 + (i * 250000))
                            for i in range(num_rows - 15)
                        ],
                        index=range(15, 250),
                    )
                ),
            ],
            ignore_index=False,
        )
    
        base_numbers = [
            (1638290040706793300 + (i * 69527182702409)) for i in range(num_rows)
        ]
    
        pdf = pd.DataFrame(
            {"dt1": dt1, "dt2": pd.to_datetime(pd.Series(base_numbers))}, index=range(250)
        )
    
        for i in range(3, 8):
            pdf[f"dt{i}"] = pd.to_datetime(
                pd.Series([int(x + (0.12 * i)) for x in base_numbers])
            )
    
        ddf = dd.from_pandas(pdf, 8)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        # Same thing but as Series. No axis, since axis=1 raises error
        assert_near_timedeltas(ddf["dt1"].std(**kwargs).compute(), pdf["dt1"].std(**kwargs))
    
        # Computation on full dataset
        expected = pdf.std(axis=axis, **kwargs)
>       result = ddf.std(axis=axis, **kwargs).compute()

dask/dataframe/tests/test_arithmetics_reduction.py:1367: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1, dt2, dt3, dt4, dt5, dt6, dt7]
Index: []
key = 'dt1', value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_datetime_std_across_axis1_null_results[False-False] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

skipna = False, numeric_only = False

    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_across_axis1_null_results(skipna, numeric_only):
        pdf = pd.DataFrame(
            {
                "dt1": [
                    datetime.fromtimestamp(1636426704 + (i * 250000)) for i in range(10)
                ],
                "dt2": [
                    datetime.fromtimestamp(1636426704 + (i * 217790)) for i in range(10)
                ],
                "nums": [i for i in range(10)],
            }
        )
    
        ddf = dd.from_pandas(pdf, 3)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        ctx = contextlib.nullcontext()
        success = True
        if numeric_only is False or numeric_only is None:
            ctx = pytest.raises(TypeError)
            success = False
        elif numeric_only is None:
            ctx = pytest.warns(FutureWarning, match="numeric_only")
    
        # Single column always results in NaT
        expected = pdf[["dt1"]].std(axis=1, **kwargs)
>       result = ddf[["dt1"]].std(axis=1, **kwargs)

dask/dataframe/tests/test_arithmetics_reduction.py:1401: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1]
Index: [], key = 'dt1'
value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_datetime_std_across_axis1_null_results[False-True] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

skipna = True, numeric_only = False

    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_across_axis1_null_results(skipna, numeric_only):
        pdf = pd.DataFrame(
            {
                "dt1": [
                    datetime.fromtimestamp(1636426704 + (i * 250000)) for i in range(10)
                ],
                "dt2": [
                    datetime.fromtimestamp(1636426704 + (i * 217790)) for i in range(10)
                ],
                "nums": [i for i in range(10)],
            }
        )
    
        ddf = dd.from_pandas(pdf, 3)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        ctx = contextlib.nullcontext()
        success = True
        if numeric_only is False or numeric_only is None:
            ctx = pytest.raises(TypeError)
            success = False
        elif numeric_only is None:
            ctx = pytest.warns(FutureWarning, match="numeric_only")
    
        # Single column always results in NaT
        expected = pdf[["dt1"]].std(axis=1, **kwargs)
>       result = ddf[["dt1"]].std(axis=1, **kwargs)

dask/dataframe/tests/test_arithmetics_reduction.py:1401: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1]
Index: [], key = 'dt1'
value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_datetime_std_across_axis1_null_results[None-False] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

skipna = False, numeric_only = None

    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_across_axis1_null_results(skipna, numeric_only):
        pdf = pd.DataFrame(
            {
                "dt1": [
                    datetime.fromtimestamp(1636426704 + (i * 250000)) for i in range(10)
                ],
                "dt2": [
                    datetime.fromtimestamp(1636426704 + (i * 217790)) for i in range(10)
                ],
                "nums": [i for i in range(10)],
            }
        )
    
        ddf = dd.from_pandas(pdf, 3)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        ctx = contextlib.nullcontext()
        success = True
        if numeric_only is False or numeric_only is None:
            ctx = pytest.raises(TypeError)
            success = False
        elif numeric_only is None:
            ctx = pytest.warns(FutureWarning, match="numeric_only")
    
        # Single column always results in NaT
        expected = pdf[["dt1"]].std(axis=1, **kwargs)
>       result = ddf[["dt1"]].std(axis=1, **kwargs)

dask/dataframe/tests/test_arithmetics_reduction.py:1401: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1]
Index: [], key = 'dt1'
value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_datetime_std_across_axis1_null_results[None-True] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

skipna = True, numeric_only = None

    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("numeric_only", [True, False, None])
    def test_datetime_std_across_axis1_null_results(skipna, numeric_only):
        pdf = pd.DataFrame(
            {
                "dt1": [
                    datetime.fromtimestamp(1636426704 + (i * 250000)) for i in range(10)
                ],
                "dt2": [
                    datetime.fromtimestamp(1636426704 + (i * 217790)) for i in range(10)
                ],
                "nums": [i for i in range(10)],
            }
        )
    
        ddf = dd.from_pandas(pdf, 3)
    
        kwargs = {} if numeric_only is None else {"numeric_only": numeric_only}
        kwargs["skipna"] = skipna
    
        ctx = contextlib.nullcontext()
        success = True
        if numeric_only is False or numeric_only is None:
            ctx = pytest.raises(TypeError)
            success = False
        elif numeric_only is None:
            ctx = pytest.warns(FutureWarning, match="numeric_only")
    
        # Single column always results in NaT
        expected = pdf[["dt1"]].std(axis=1, **kwargs)
>       result = ddf[["dt1"]].std(axis=1, **kwargs)

dask/dataframe/tests/test_arithmetics_reduction.py:1401: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1569: in std
    numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [dt1]
Index: [], key = 'dt1'
value = Series([], Name: dt1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________________ test_groupby_apply ______________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
0              int64  ...  ...
75               ...    ...    ...
99               ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]

    @pytest.mark.filterwarnings("ignore:DataFrameGroupBy.apply operated ")
    def test_groupby_apply(df, pdf):
        def test(x):
            x["new"] = x.sum().sum()
            return x
    
        with pytest.warns(UserWarning, match="inferred from partial data"):
>           assert_eq(df.groupby(df.x).apply(test), pdf.groupby(pdf.x).apply(test))

dask/dataframe/dask_expr/tests/test_groupby.py:403: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   x      y      z    new
npartitions=4                            
        ...erved=False, func=<function test_groupby_apply.<locals>.test at 0x7ffb6b4b21f0>, meta=<no_default>, args=(), kwargs={})
b =       x   y  z  new
x                  
0 0   0   0  1  460
  10  0  10  1  460
  20  0  20  1  460
  30  0  30  1  46...  9  59  1  640
  69  9  69  1  640
  79  9  79  1  640
  89  9  89  1  640
  99  9  99  1  640

[100 rows x 4 columns]
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   x      y      z    new
npartitions=4                            
        ...erved=False, func=<function test_groupby_apply.<locals>.test at 0x7ffb6b4b21f0>, meta=<no_default>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_groupby_apply.<locals>.test at 0x7ffb6b4b21f0>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-9f9763b8a5faf1b7ddeee17b40fef806', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
____________________________ test_groupby_transform ____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
0              int64  ...  ...
75               ...    ...    ...
99               ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]

    @pytest.mark.filterwarnings("ignore:DataFrameGroupBy.apply operated ")
    def test_groupby_transform(df, pdf):
        def test(x):
            return x
    
        with pytest.warns(UserWarning, match="inferred from partial data"):
>           assert_eq(df.groupby(df.x).transform(test), pdf.groupby(pdf.x).transform(test))

dask/dataframe/dask_expr/tests/test_groupby.py:439: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   y      z
npartitions=4              
               int64  int64
        ...d=False, func=<function test_groupby_transform.<locals>.test at 0x7ffb6b4b0e00>, meta=<no_default>, args=(), kwargs={})
b =      y  z
0    0  1
1    1  1
2    2  1
3    3  1
4    4  1
..  .. ..
95  95  1
96  96  1
97  97  1
98  98  1
99  99  1

[100 rows x 2 columns]
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   y      z
npartitions=4              
               int64  int64
        ...d=False, func=<function test_groupby_transform.<locals>.test at 0x7ffb6b4b0e00>, meta=<no_default>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_groupby_transform.<locals>.test at 0x7ffb6b4b0e00>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-d8515154329829420652ee79891c5b0b', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
___________________ test_groupby_numeric_only_lambda_caller ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
0              int64  ...  ...
75               ...    ...    ...
99               ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]

    def test_groupby_numeric_only_lambda_caller(df, pdf):
>       assert_eq(
            df.groupby(lambda x: x // 2).mean(numeric_only=False),
            pdf.groupby(lambda x: x // 2).mean(numeric_only=False),
        )

dask/dataframe/dask_expr/tests/test_groupby.py:492: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                     x        y        z
npartitions=1                           
          ...xpressions
Expr=df.mean(observed=False, chunk_kwargs={'numeric_only': False}, aggregate_kwargs={'numeric_only': False})
b =       x     y    z
0   0.5   0.5  1.0
1   2.5   2.5  1.0
2   4.5   4.5  1.0
3   6.5   6.5  1.0
4   8.5   8.5  1.0
5   ...  1.0
44  8.5  88.5  1.0
45  0.5  90.5  1.0
46  2.5  92.5  1.0
47  4.5  94.5  1.0
48  6.5  96.5  1.0
49  8.5  98.5  1.0
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                     x        y        z
npartitions=1                           
          ...xpressions
Expr=df.mean(observed=False, chunk_kwargs={'numeric_only': False}, aggregate_kwargs={'numeric_only': False})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_groupby_numeric_only_lambda_caller.<locals>.<lambda> at 0x7ffb68b750c0>
E                           when serializing tuple item 1
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_mean_chunk-b772c1eeb342115acef13ee47d4e02bb', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_______________________________ test_categorize ________________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_categorize():
        # rename y to y_ to avoid pandas future warning about ambiguous
        # levels
        pdf = frames4[0]
        if pyarrow_strings_enabled():
            # we explicitly provide meta, so it has to have pyarrow strings
            pdf = to_pyarrow_string(pdf)
        dsk = {("unknown", i): df for (i, df) in enumerate(frames3)}
        pdf = (
            pd.concat(dsk.values())
            .rename(columns={"y": "y_"})
            .astype({"w": "category", "y_": "category"})
        )
        pdf.index = pdf.index.astype("category")
        ddf = dd.from_pandas(pdf, npartitions=4, sort=False)
>       ddf["w"] = ddf.w.cat.as_unknown()

dask/dataframe/tests/test_categorical.py:206: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [v, w, x, y_, z]
Index: [], key = 'w'
value = Series([], Name: w, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_categorical_set_index[disk] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'disk'

    def test_categorical_set_index(shuffle_method):
        df = pd.DataFrame({"x": [1, 2, 3, 4], "y": ["a", "b", "b", "c"]})
>       df["y"] = pd.Categorical(df["y"], categories=["a", "b", "c"], ordered=True)

dask/dataframe/tests/test_categorical.py:316: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x  y
0  1  a
1  2  b
2  3  b
3  4  c, key = 'y'
value = ['a', 'b', 'b', 'c']
Categories (3, object): ['a' < 'b' < 'c']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_categorical_set_index[tasks] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks'

    def test_categorical_set_index(shuffle_method):
        df = pd.DataFrame({"x": [1, 2, 3, 4], "y": ["a", "b", "b", "c"]})
>       df["y"] = pd.Categorical(df["y"], categories=["a", "b", "c"], ordered=True)

dask/dataframe/tests/test_categorical.py:316: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x  y
0  1  a
1  2  b
2  3  b
3  4  c, key = 'y'
value = ['a', 'b', 'b', 'c']
Categories (3, object): ['a' < 'b' < 'c']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_categorical_set_index_npartitions_vs_ncategories[1-1] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, ncategories = 1

    @pytest.mark.parametrize("ncategories", [1, 3, 6])
    @pytest.mark.parametrize("npartitions", [1, 3, 6])
    def test_categorical_set_index_npartitions_vs_ncategories(npartitions, ncategories):
        """https://github.com/dask/dask/issues/5343"""
        rows_per_category = 10
        n_rows = ncategories * rows_per_category
    
        categories = ["CAT" + str(i) for i in range(ncategories)]
        pdf = pd.DataFrame(
            {"id": categories * rows_per_category, "value": np.random.random(n_rows)}
        )
        ddf = dd.from_pandas(pdf, npartitions=npartitions)
>       ddf["id"] = ddf["id"].astype("category").cat.as_ordered()

dask/dataframe/tests/test_categorical.py:348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [id, value]
Index: [], key = 'id'
value = Series([], Name: id, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_categorical_set_index_npartitions_vs_ncategories[1-3] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, ncategories = 3

    @pytest.mark.parametrize("ncategories", [1, 3, 6])
    @pytest.mark.parametrize("npartitions", [1, 3, 6])
    def test_categorical_set_index_npartitions_vs_ncategories(npartitions, ncategories):
        """https://github.com/dask/dask/issues/5343"""
        rows_per_category = 10
        n_rows = ncategories * rows_per_category
    
        categories = ["CAT" + str(i) for i in range(ncategories)]
        pdf = pd.DataFrame(
            {"id": categories * rows_per_category, "value": np.random.random(n_rows)}
        )
        ddf = dd.from_pandas(pdf, npartitions=npartitions)
>       ddf["id"] = ddf["id"].astype("category").cat.as_ordered()

dask/dataframe/tests/test_categorical.py:348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [id, value]
Index: [], key = 'id'
value = Series([], Name: id, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_categorical_set_index_npartitions_vs_ncategories[1-6] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, ncategories = 6

    @pytest.mark.parametrize("ncategories", [1, 3, 6])
    @pytest.mark.parametrize("npartitions", [1, 3, 6])
    def test_categorical_set_index_npartitions_vs_ncategories(npartitions, ncategories):
        """https://github.com/dask/dask/issues/5343"""
        rows_per_category = 10
        n_rows = ncategories * rows_per_category
    
        categories = ["CAT" + str(i) for i in range(ncategories)]
        pdf = pd.DataFrame(
            {"id": categories * rows_per_category, "value": np.random.random(n_rows)}
        )
        ddf = dd.from_pandas(pdf, npartitions=npartitions)
>       ddf["id"] = ddf["id"].astype("category").cat.as_ordered()

dask/dataframe/tests/test_categorical.py:348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [id, value]
Index: [], key = 'id'
value = Series([], Name: id, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_categorical_set_index_npartitions_vs_ncategories[3-1] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 3, ncategories = 1

    @pytest.mark.parametrize("ncategories", [1, 3, 6])
    @pytest.mark.parametrize("npartitions", [1, 3, 6])
    def test_categorical_set_index_npartitions_vs_ncategories(npartitions, ncategories):
        """https://github.com/dask/dask/issues/5343"""
        rows_per_category = 10
        n_rows = ncategories * rows_per_category
    
        categories = ["CAT" + str(i) for i in range(ncategories)]
        pdf = pd.DataFrame(
            {"id": categories * rows_per_category, "value": np.random.random(n_rows)}
        )
        ddf = dd.from_pandas(pdf, npartitions=npartitions)
>       ddf["id"] = ddf["id"].astype("category").cat.as_ordered()

dask/dataframe/tests/test_categorical.py:348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [id, value]
Index: [], key = 'id'
value = Series([], Name: id, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_categorical_set_index_npartitions_vs_ncategories[3-3] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 3, ncategories = 3

    @pytest.mark.parametrize("ncategories", [1, 3, 6])
    @pytest.mark.parametrize("npartitions", [1, 3, 6])
    def test_categorical_set_index_npartitions_vs_ncategories(npartitions, ncategories):
        """https://github.com/dask/dask/issues/5343"""
        rows_per_category = 10
        n_rows = ncategories * rows_per_category
    
        categories = ["CAT" + str(i) for i in range(ncategories)]
        pdf = pd.DataFrame(
            {"id": categories * rows_per_category, "value": np.random.random(n_rows)}
        )
        ddf = dd.from_pandas(pdf, npartitions=npartitions)
>       ddf["id"] = ddf["id"].astype("category").cat.as_ordered()

dask/dataframe/tests/test_categorical.py:348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [id, value]
Index: [], key = 'id'
value = Series([], Name: id, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_apply_or_transform_shuffle_multilevel[<lambda>0-True] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

pdf =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]
df = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
0              int64  ...  ...
75               ...    ...    ...
99               ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
func = <function <lambda> at 0x7ffb942a1220>, cow = True

    @pytest.mark.parametrize("cow", [True, False])
    @pytest.mark.parametrize(
        "func",
        [
            lambda grouped: grouped.apply(lambda x: x.sum()),
            lambda grouped: grouped.transform(lambda x: x.sum()),
        ],
    )
    def test_apply_or_transform_shuffle_multilevel(pdf, df, func, cow):
        with pd.option_context("mode.copy_on_write", cow):
            grouper = lambda df: [df["x"] + 1, df["y"] + 1]
    
            with pytest.warns(UserWarning):
                # DataFrameGroupBy
>               assert_eq(func(df.groupby(grouper(df))), func(pdf.groupby(grouper(pdf))))

dask/dataframe/dask_expr/tests/test_groupby.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ...f, observed=False, func=<function <lambda>.<locals>.<lambda> at 0x7ffb68b74e00>, meta=<no_default>, args=(), kwargs={})
b =         x   y  z
x  y            
1  1    0   0  1
   11   0  10  1
   21   0  20  1
   31   0  30  1
   41   0  40  1... ..  .. ..
10 60   9  59  1
   70   9  69  1
   80   9  79  1
   90   9  89  1
   100  9  99  1

[100 rows x 3 columns]
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ...f, observed=False, func=<function <lambda>.<locals>.<lambda> at 0x7ffb68b74e00>, meta=<no_default>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function <lambda>.<locals>.<lambda> at 0x7ffb68b74e00>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-9caa5e86dde399f8c78b35d964027ee8', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_________ test_apply_or_transform_shuffle_multilevel[<lambda>0-False] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

pdf =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]
df = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
0              int64  ...  ...
75               ...    ...    ...
99               ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
func = <function <lambda> at 0x7ffb942a1220>, cow = False

    @pytest.mark.parametrize("cow", [True, False])
    @pytest.mark.parametrize(
        "func",
        [
            lambda grouped: grouped.apply(lambda x: x.sum()),
            lambda grouped: grouped.transform(lambda x: x.sum()),
        ],
    )
    def test_apply_or_transform_shuffle_multilevel(pdf, df, func, cow):
        with pd.option_context("mode.copy_on_write", cow):
            grouper = lambda df: [df["x"] + 1, df["y"] + 1]
    
            with pytest.warns(UserWarning):
                # DataFrameGroupBy
>               assert_eq(func(df.groupby(grouper(df))), func(pdf.groupby(grouper(pdf))))

dask/dataframe/dask_expr/tests/test_groupby.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ...f, observed=False, func=<function <lambda>.<locals>.<lambda> at 0x7ffb68b74e00>, meta=<no_default>, args=(), kwargs={})
b =         x   y  z
x  y            
1  1    0   0  1
   11   0  10  1
   21   0  20  1
   31   0  30  1
   41   0  40  1... ..  .. ..
10 60   9  59  1
   70   9  69  1
   80   9  79  1
   90   9  89  1
   100  9  99  1

[100 rows x 3 columns]
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ...f, observed=False, func=<function <lambda>.<locals>.<lambda> at 0x7ffb68b74e00>, meta=<no_default>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function <lambda>.<locals>.<lambda> at 0x7ffb68b74e00>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-9caa5e86dde399f8c78b35d964027ee8', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
__________ test_categorical_set_index_npartitions_vs_ncategories[3-6] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 3, ncategories = 6

    @pytest.mark.parametrize("ncategories", [1, 3, 6])
    @pytest.mark.parametrize("npartitions", [1, 3, 6])
    def test_categorical_set_index_npartitions_vs_ncategories(npartitions, ncategories):
        """https://github.com/dask/dask/issues/5343"""
        rows_per_category = 10
        n_rows = ncategories * rows_per_category
    
        categories = ["CAT" + str(i) for i in range(ncategories)]
        pdf = pd.DataFrame(
            {"id": categories * rows_per_category, "value": np.random.random(n_rows)}
        )
        ddf = dd.from_pandas(pdf, npartitions=npartitions)
>       ddf["id"] = ddf["id"].astype("category").cat.as_ordered()

dask/dataframe/tests/test_categorical.py:348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [id, value]
Index: [], key = 'id'
value = Series([], Name: id, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_apply_or_transform_shuffle_multilevel[<lambda>1-True] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

pdf =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]
df = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
0              int64  ...  ...
75               ...    ...    ...
99               ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
func = <function <lambda> at 0x7ffb942a12d0>, cow = True

    @pytest.mark.parametrize("cow", [True, False])
    @pytest.mark.parametrize(
        "func",
        [
            lambda grouped: grouped.apply(lambda x: x.sum()),
            lambda grouped: grouped.transform(lambda x: x.sum()),
        ],
    )
    def test_apply_or_transform_shuffle_multilevel(pdf, df, func, cow):
        with pd.option_context("mode.copy_on_write", cow):
            grouper = lambda df: [df["x"] + 1, df["y"] + 1]
    
            with pytest.warns(UserWarning):
                # DataFrameGroupBy
>               assert_eq(func(df.groupby(grouper(df))), func(pdf.groupby(grouper(pdf))))

dask/dataframe/dask_expr/tests/test_groupby.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ...f, observed=False, func=<function <lambda>.<locals>.<lambda> at 0x7ffb68b75380>, meta=<no_default>, args=(), kwargs={})
b =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ...f, observed=False, func=<function <lambda>.<locals>.<lambda> at 0x7ffb68b75380>, meta=<no_default>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function <lambda>.<locals>.<lambda> at 0x7ffb68b75380>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-ecef59697688b9bbb4fdc88c7f956a94', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_________ test_apply_or_transform_shuffle_multilevel[<lambda>1-False] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

pdf =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]
df = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
0              int64  ...  ...
75               ...    ...    ...
99               ...    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
func = <function <lambda> at 0x7ffb942a12d0>, cow = False

    @pytest.mark.parametrize("cow", [True, False])
    @pytest.mark.parametrize(
        "func",
        [
            lambda grouped: grouped.apply(lambda x: x.sum()),
            lambda grouped: grouped.transform(lambda x: x.sum()),
        ],
    )
    def test_apply_or_transform_shuffle_multilevel(pdf, df, func, cow):
        with pd.option_context("mode.copy_on_write", cow):
            grouper = lambda df: [df["x"] + 1, df["y"] + 1]
    
            with pytest.warns(UserWarning):
                # DataFrameGroupBy
>               assert_eq(func(df.groupby(grouper(df))), func(pdf.groupby(grouper(pdf))))

dask/dataframe/dask_expr/tests/test_groupby.py:535: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ...f, observed=False, func=<function <lambda>.<locals>.<lambda> at 0x7ffb68b75380>, meta=<no_default>, args=(), kwargs={})
b =     x   y  z
0   0   0  1
1   1   1  1
2   2   2  1
3   3   3  1
4   4   4  1
.. ..  .. ..
95  5  95  1
96  6  96  1
97  7  97  1
98  8  98  1
99  9  99  1

[100 rows x 3 columns]
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                   x      y      z
npartitions=4                     
               int64  ...f, observed=False, func=<function <lambda>.<locals>.<lambda> at 0x7ffb68b75380>, meta=<no_default>, args=(), kwargs={})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function <lambda>.<locals>.<lambda> at 0x7ffb68b75380>
E                           when serializing dict item 'func'
E                           when serializing tuple item 2
E                           when serializing functools.partial state
E                           when serializing functools.partial object
E                           when serializing dict item 'dask_func'
E                           when serializing tuple item 8
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('operation-ecef59697688b9bbb4fdc88c7f956a94', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
__________ test_categorical_set_index_npartitions_vs_ncategories[6-1] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 6, ncategories = 1

    @pytest.mark.parametrize("ncategories", [1, 3, 6])
    @pytest.mark.parametrize("npartitions", [1, 3, 6])
    def test_categorical_set_index_npartitions_vs_ncategories(npartitions, ncategories):
        """https://github.com/dask/dask/issues/5343"""
        rows_per_category = 10
        n_rows = ncategories * rows_per_category
    
        categories = ["CAT" + str(i) for i in range(ncategories)]
        pdf = pd.DataFrame(
            {"id": categories * rows_per_category, "value": np.random.random(n_rows)}
        )
        ddf = dd.from_pandas(pdf, npartitions=npartitions)
>       ddf["id"] = ddf["id"].astype("category").cat.as_ordered()

dask/dataframe/tests/test_categorical.py:348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [id, value]
Index: [], key = 'id'
value = Series([], Name: id, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_categorical_set_index_npartitions_vs_ncategories[6-3] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 6, ncategories = 3

    @pytest.mark.parametrize("ncategories", [1, 3, 6])
    @pytest.mark.parametrize("npartitions", [1, 3, 6])
    def test_categorical_set_index_npartitions_vs_ncategories(npartitions, ncategories):
        """https://github.com/dask/dask/issues/5343"""
        rows_per_category = 10
        n_rows = ncategories * rows_per_category
    
        categories = ["CAT" + str(i) for i in range(ncategories)]
        pdf = pd.DataFrame(
            {"id": categories * rows_per_category, "value": np.random.random(n_rows)}
        )
        ddf = dd.from_pandas(pdf, npartitions=npartitions)
>       ddf["id"] = ddf["id"].astype("category").cat.as_ordered()

dask/dataframe/tests/test_categorical.py:348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [id, value]
Index: [], key = 'id'
value = Series([], Name: id, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_categorical_set_index_npartitions_vs_ncategories[6-6] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 6, ncategories = 6

    @pytest.mark.parametrize("ncategories", [1, 3, 6])
    @pytest.mark.parametrize("npartitions", [1, 3, 6])
    def test_categorical_set_index_npartitions_vs_ncategories(npartitions, ncategories):
        """https://github.com/dask/dask/issues/5343"""
        rows_per_category = 10
        n_rows = ncategories * rows_per_category
    
        categories = ["CAT" + str(i) for i in range(ncategories)]
        pdf = pd.DataFrame(
            {"id": categories * rows_per_category, "value": np.random.random(n_rows)}
        )
        ddf = dd.from_pandas(pdf, npartitions=npartitions)
>       ddf["id"] = ddf["id"].astype("category").cat.as_ordered()

dask/dataframe/tests/test_categorical.py:348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [id, value]
Index: [], key = 'id'
value = Series([], Name: id, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_repartition_on_categoricals[1] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1

    @pytest.mark.parametrize("npartitions", [1, 4])
    def test_repartition_on_categoricals(npartitions):
        df = pd.DataFrame({"x": range(10), "y": list("abababcbcb")})
        if pyarrow_strings_enabled():
            # we need this because a CategoricalDtype backed by arrow strings
            # is not the same as CategoricalDtype backed by object strings
            df = to_pyarrow_string(df)
    
        ddf = dd.from_pandas(df, npartitions=2)
>       ddf["y"] = ddf["y"].astype("category")

dask/dataframe/tests/test_categorical.py:362: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_repartition_on_categoricals[4] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 4

    @pytest.mark.parametrize("npartitions", [1, 4])
    def test_repartition_on_categoricals(npartitions):
        df = pd.DataFrame({"x": range(10), "y": list("abababcbcb")})
        if pyarrow_strings_enabled():
            # we need this because a CategoricalDtype backed by arrow strings
            # is not the same as CategoricalDtype backed by object strings
            df = to_pyarrow_string(df)
    
        ddf = dd.from_pandas(df, npartitions=2)
>       ddf["y"] = ddf["y"].astype("category")

dask/dataframe/tests/test_categorical.py:362: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________________ test_categorize_nan ______________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_categorize_nan():
        df = dd.from_pandas(
            pd.DataFrame({"A": ["a", "b", "a", float("nan")]}), npartitions=2
        )
        with warnings.catch_warnings(record=True) as record:
>           df.categorize().compute()

dask/dataframe/tests/test_categorical.py:392: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3802: in categorize
    return new_collection(Categorize(self, categories, index))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_categorical.py:175: in _meta
    return _categorize_block(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/categorical.py:25: in _categorize_block
    df[col] = df[col].astype(cat_dtype)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A]
Index: [], key = 'A'
value = Series([], Name: A, dtype: category
Categories (2, string): [a, b])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_return_type_known_categories _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_return_type_known_categories():
        df = pd.DataFrame({"A": ["a", "b", "c"]})
>       df["A"] = df["A"].astype("category")

dask/dataframe/tests/test_categorical.py:411: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    A
0  a
1  b
2  c, key = 'A'
value = 0    a
1    b
2    c
Name: A, dtype: category
Categories (3, object): ['a', 'b', 'c']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_describe[None-None-None-subset0] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = None, exclude = None, percentiles = None, subset = ['c', 'd']

    @pytest.mark.parametrize(
        "include,exclude,percentiles,subset",
        [
            (None, None, None, ["c", "d"]),  # numeric
            (None, None, None, ["c", "d", "f"]),  # numeric + timedelta
            (None, None, None, ["c", "d", "g"]),  # numeric + bool
            (None, None, None, ["c", "d", "f", "g"]),  # numeric + bool + timedelta
            (None, None, None, ["f", "g"]),  # bool + timedelta
            ("all", None, None, None),
            (["number"], None, [0.25, 0.5], None),
            ([np.timedelta64], None, None, None),
            (["number", get_string_dtype()], None, [0.25, 0.75], None),
            (None, ["number", get_string_dtype()], None, None),
            ([get_string_dtype(), "datetime", "bool"], None, None, None),
        ],
    )
    def test_describe(include, exclude, percentiles, subset):
        data = {
            "a": ["aaa", "bbb", "bbb", None, None, "zzz"] * 2,
            "c": [None, 0, 1, 2, 3, 4] * 2,
            "d": [None, 0, 1] * 4,
            "e": [
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 07:56:23.858694"),
                pd.Timestamp("2017-05-09 05:59:58.938999"),
                None,
                None,
            ]
            * 2,
            "f": [
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
                None,
                None,
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
            ]
            * 2,
            "g": [True, False, True] * 4,
        }
    
        # Arrange
        df = pd.DataFrame(data)
>       df["a"] = df["a"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:429: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        a    c    d                          e      f      g
0    aaa  NaN  NaN 2017-05-09 00:00:00.006000 3 days   Tru...10  None  3.0  0.0                        NaT 3 days  False
11   zzz  4.0  1.0                        NaT 1 days   True
key = 'a'
value = 0      aaa
1      bbb
2      bbb
3     <NA>
4     <NA>
5      zzz
6      aaa
7      bbb
8      bbb
9     <NA>
10    <NA>
11     zzz
Name: a, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_describe[None-None-None-subset1] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = None, exclude = None, percentiles = None, subset = ['c', 'd', 'f']

    @pytest.mark.parametrize(
        "include,exclude,percentiles,subset",
        [
            (None, None, None, ["c", "d"]),  # numeric
            (None, None, None, ["c", "d", "f"]),  # numeric + timedelta
            (None, None, None, ["c", "d", "g"]),  # numeric + bool
            (None, None, None, ["c", "d", "f", "g"]),  # numeric + bool + timedelta
            (None, None, None, ["f", "g"]),  # bool + timedelta
            ("all", None, None, None),
            (["number"], None, [0.25, 0.5], None),
            ([np.timedelta64], None, None, None),
            (["number", get_string_dtype()], None, [0.25, 0.75], None),
            (None, ["number", get_string_dtype()], None, None),
            ([get_string_dtype(), "datetime", "bool"], None, None, None),
        ],
    )
    def test_describe(include, exclude, percentiles, subset):
        data = {
            "a": ["aaa", "bbb", "bbb", None, None, "zzz"] * 2,
            "c": [None, 0, 1, 2, 3, 4] * 2,
            "d": [None, 0, 1] * 4,
            "e": [
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 07:56:23.858694"),
                pd.Timestamp("2017-05-09 05:59:58.938999"),
                None,
                None,
            ]
            * 2,
            "f": [
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
                None,
                None,
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
            ]
            * 2,
            "g": [True, False, True] * 4,
        }
    
        # Arrange
        df = pd.DataFrame(data)
>       df["a"] = df["a"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:429: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        a    c    d                          e      f      g
0    aaa  NaN  NaN 2017-05-09 00:00:00.006000 3 days   Tru...10  None  3.0  0.0                        NaT 3 days  False
11   zzz  4.0  1.0                        NaT 1 days   True
key = 'a'
value = 0      aaa
1      bbb
2      bbb
3     <NA>
4     <NA>
5      zzz
6      aaa
7      bbb
8      bbb
9     <NA>
10    <NA>
11     zzz
Name: a, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_describe[None-None-None-subset2] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = None, exclude = None, percentiles = None, subset = ['c', 'd', 'g']

    @pytest.mark.parametrize(
        "include,exclude,percentiles,subset",
        [
            (None, None, None, ["c", "d"]),  # numeric
            (None, None, None, ["c", "d", "f"]),  # numeric + timedelta
            (None, None, None, ["c", "d", "g"]),  # numeric + bool
            (None, None, None, ["c", "d", "f", "g"]),  # numeric + bool + timedelta
            (None, None, None, ["f", "g"]),  # bool + timedelta
            ("all", None, None, None),
            (["number"], None, [0.25, 0.5], None),
            ([np.timedelta64], None, None, None),
            (["number", get_string_dtype()], None, [0.25, 0.75], None),
            (None, ["number", get_string_dtype()], None, None),
            ([get_string_dtype(), "datetime", "bool"], None, None, None),
        ],
    )
    def test_describe(include, exclude, percentiles, subset):
        data = {
            "a": ["aaa", "bbb", "bbb", None, None, "zzz"] * 2,
            "c": [None, 0, 1, 2, 3, 4] * 2,
            "d": [None, 0, 1] * 4,
            "e": [
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 07:56:23.858694"),
                pd.Timestamp("2017-05-09 05:59:58.938999"),
                None,
                None,
            ]
            * 2,
            "f": [
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
                None,
                None,
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
            ]
            * 2,
            "g": [True, False, True] * 4,
        }
    
        # Arrange
        df = pd.DataFrame(data)
>       df["a"] = df["a"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:429: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        a    c    d                          e      f      g
0    aaa  NaN  NaN 2017-05-09 00:00:00.006000 3 days   Tru...10  None  3.0  0.0                        NaT 3 days  False
11   zzz  4.0  1.0                        NaT 1 days   True
key = 'a'
value = 0      aaa
1      bbb
2      bbb
3     <NA>
4     <NA>
5      zzz
6      aaa
7      bbb
8      bbb
9     <NA>
10    <NA>
11     zzz
Name: a, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_describe[None-None-None-subset3] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = None, exclude = None, percentiles = None
subset = ['c', 'd', 'f', 'g']

    @pytest.mark.parametrize(
        "include,exclude,percentiles,subset",
        [
            (None, None, None, ["c", "d"]),  # numeric
            (None, None, None, ["c", "d", "f"]),  # numeric + timedelta
            (None, None, None, ["c", "d", "g"]),  # numeric + bool
            (None, None, None, ["c", "d", "f", "g"]),  # numeric + bool + timedelta
            (None, None, None, ["f", "g"]),  # bool + timedelta
            ("all", None, None, None),
            (["number"], None, [0.25, 0.5], None),
            ([np.timedelta64], None, None, None),
            (["number", get_string_dtype()], None, [0.25, 0.75], None),
            (None, ["number", get_string_dtype()], None, None),
            ([get_string_dtype(), "datetime", "bool"], None, None, None),
        ],
    )
    def test_describe(include, exclude, percentiles, subset):
        data = {
            "a": ["aaa", "bbb", "bbb", None, None, "zzz"] * 2,
            "c": [None, 0, 1, 2, 3, 4] * 2,
            "d": [None, 0, 1] * 4,
            "e": [
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 07:56:23.858694"),
                pd.Timestamp("2017-05-09 05:59:58.938999"),
                None,
                None,
            ]
            * 2,
            "f": [
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
                None,
                None,
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
            ]
            * 2,
            "g": [True, False, True] * 4,
        }
    
        # Arrange
        df = pd.DataFrame(data)
>       df["a"] = df["a"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:429: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        a    c    d                          e      f      g
0    aaa  NaN  NaN 2017-05-09 00:00:00.006000 3 days   Tru...10  None  3.0  0.0                        NaT 3 days  False
11   zzz  4.0  1.0                        NaT 1 days   True
key = 'a'
value = 0      aaa
1      bbb
2      bbb
3     <NA>
4     <NA>
5      zzz
6      aaa
7      bbb
8      bbb
9     <NA>
10    <NA>
11     zzz
Name: a, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_describe[None-None-None-subset4] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = None, exclude = None, percentiles = None, subset = ['f', 'g']

    @pytest.mark.parametrize(
        "include,exclude,percentiles,subset",
        [
            (None, None, None, ["c", "d"]),  # numeric
            (None, None, None, ["c", "d", "f"]),  # numeric + timedelta
            (None, None, None, ["c", "d", "g"]),  # numeric + bool
            (None, None, None, ["c", "d", "f", "g"]),  # numeric + bool + timedelta
            (None, None, None, ["f", "g"]),  # bool + timedelta
            ("all", None, None, None),
            (["number"], None, [0.25, 0.5], None),
            ([np.timedelta64], None, None, None),
            (["number", get_string_dtype()], None, [0.25, 0.75], None),
            (None, ["number", get_string_dtype()], None, None),
            ([get_string_dtype(), "datetime", "bool"], None, None, None),
        ],
    )
    def test_describe(include, exclude, percentiles, subset):
        data = {
            "a": ["aaa", "bbb", "bbb", None, None, "zzz"] * 2,
            "c": [None, 0, 1, 2, 3, 4] * 2,
            "d": [None, 0, 1] * 4,
            "e": [
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 07:56:23.858694"),
                pd.Timestamp("2017-05-09 05:59:58.938999"),
                None,
                None,
            ]
            * 2,
            "f": [
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
                None,
                None,
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
            ]
            * 2,
            "g": [True, False, True] * 4,
        }
    
        # Arrange
        df = pd.DataFrame(data)
>       df["a"] = df["a"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:429: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        a    c    d                          e      f      g
0    aaa  NaN  NaN 2017-05-09 00:00:00.006000 3 days   Tru...10  None  3.0  0.0                        NaT 3 days  False
11   zzz  4.0  1.0                        NaT 1 days   True
key = 'a'
value = 0      aaa
1      bbb
2      bbb
3     <NA>
4     <NA>
5      zzz
6      aaa
7      bbb
8      bbb
9     <NA>
10    <NA>
11     zzz
Name: a, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_describe[all-None-None-None] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = 'all', exclude = None, percentiles = None, subset = None

    @pytest.mark.parametrize(
        "include,exclude,percentiles,subset",
        [
            (None, None, None, ["c", "d"]),  # numeric
            (None, None, None, ["c", "d", "f"]),  # numeric + timedelta
            (None, None, None, ["c", "d", "g"]),  # numeric + bool
            (None, None, None, ["c", "d", "f", "g"]),  # numeric + bool + timedelta
            (None, None, None, ["f", "g"]),  # bool + timedelta
            ("all", None, None, None),
            (["number"], None, [0.25, 0.5], None),
            ([np.timedelta64], None, None, None),
            (["number", get_string_dtype()], None, [0.25, 0.75], None),
            (None, ["number", get_string_dtype()], None, None),
            ([get_string_dtype(), "datetime", "bool"], None, None, None),
        ],
    )
    def test_describe(include, exclude, percentiles, subset):
        data = {
            "a": ["aaa", "bbb", "bbb", None, None, "zzz"] * 2,
            "c": [None, 0, 1, 2, 3, 4] * 2,
            "d": [None, 0, 1] * 4,
            "e": [
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 07:56:23.858694"),
                pd.Timestamp("2017-05-09 05:59:58.938999"),
                None,
                None,
            ]
            * 2,
            "f": [
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
                None,
                None,
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
            ]
            * 2,
            "g": [True, False, True] * 4,
        }
    
        # Arrange
        df = pd.DataFrame(data)
>       df["a"] = df["a"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:429: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        a    c    d                          e      f      g
0    aaa  NaN  NaN 2017-05-09 00:00:00.006000 3 days   Tru...10  None  3.0  0.0                        NaT 3 days  False
11   zzz  4.0  1.0                        NaT 1 days   True
key = 'a'
value = 0      aaa
1      bbb
2      bbb
3     <NA>
4     <NA>
5      zzz
6      aaa
7      bbb
8      bbb
9     <NA>
10    <NA>
11     zzz
Name: a, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_describe[include6-None-percentiles6-None] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = ['number'], exclude = None, percentiles = [0.25, 0.5], subset = None

    @pytest.mark.parametrize(
        "include,exclude,percentiles,subset",
        [
            (None, None, None, ["c", "d"]),  # numeric
            (None, None, None, ["c", "d", "f"]),  # numeric + timedelta
            (None, None, None, ["c", "d", "g"]),  # numeric + bool
            (None, None, None, ["c", "d", "f", "g"]),  # numeric + bool + timedelta
            (None, None, None, ["f", "g"]),  # bool + timedelta
            ("all", None, None, None),
            (["number"], None, [0.25, 0.5], None),
            ([np.timedelta64], None, None, None),
            (["number", get_string_dtype()], None, [0.25, 0.75], None),
            (None, ["number", get_string_dtype()], None, None),
            ([get_string_dtype(), "datetime", "bool"], None, None, None),
        ],
    )
    def test_describe(include, exclude, percentiles, subset):
        data = {
            "a": ["aaa", "bbb", "bbb", None, None, "zzz"] * 2,
            "c": [None, 0, 1, 2, 3, 4] * 2,
            "d": [None, 0, 1] * 4,
            "e": [
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 07:56:23.858694"),
                pd.Timestamp("2017-05-09 05:59:58.938999"),
                None,
                None,
            ]
            * 2,
            "f": [
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
                None,
                None,
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
            ]
            * 2,
            "g": [True, False, True] * 4,
        }
    
        # Arrange
        df = pd.DataFrame(data)
>       df["a"] = df["a"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:429: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        a    c    d                          e      f      g
0    aaa  NaN  NaN 2017-05-09 00:00:00.006000 3 days   Tru...10  None  3.0  0.0                        NaT 3 days  False
11   zzz  4.0  1.0                        NaT 1 days   True
key = 'a'
value = 0      aaa
1      bbb
2      bbb
3     <NA>
4     <NA>
5      zzz
6      aaa
7      bbb
8      bbb
9     <NA>
10    <NA>
11     zzz
Name: a, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_describe[include7-None-None-None] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = [<class 'numpy.timedelta64'>], exclude = None, percentiles = None
subset = None

    @pytest.mark.parametrize(
        "include,exclude,percentiles,subset",
        [
            (None, None, None, ["c", "d"]),  # numeric
            (None, None, None, ["c", "d", "f"]),  # numeric + timedelta
            (None, None, None, ["c", "d", "g"]),  # numeric + bool
            (None, None, None, ["c", "d", "f", "g"]),  # numeric + bool + timedelta
            (None, None, None, ["f", "g"]),  # bool + timedelta
            ("all", None, None, None),
            (["number"], None, [0.25, 0.5], None),
            ([np.timedelta64], None, None, None),
            (["number", get_string_dtype()], None, [0.25, 0.75], None),
            (None, ["number", get_string_dtype()], None, None),
            ([get_string_dtype(), "datetime", "bool"], None, None, None),
        ],
    )
    def test_describe(include, exclude, percentiles, subset):
        data = {
            "a": ["aaa", "bbb", "bbb", None, None, "zzz"] * 2,
            "c": [None, 0, 1, 2, 3, 4] * 2,
            "d": [None, 0, 1] * 4,
            "e": [
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 07:56:23.858694"),
                pd.Timestamp("2017-05-09 05:59:58.938999"),
                None,
                None,
            ]
            * 2,
            "f": [
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
                None,
                None,
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
            ]
            * 2,
            "g": [True, False, True] * 4,
        }
    
        # Arrange
        df = pd.DataFrame(data)
>       df["a"] = df["a"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:429: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        a    c    d                          e      f      g
0    aaa  NaN  NaN 2017-05-09 00:00:00.006000 3 days   Tru...10  None  3.0  0.0                        NaT 3 days  False
11   zzz  4.0  1.0                        NaT 1 days   True
key = 'a'
value = 0      aaa
1      bbb
2      bbb
3     <NA>
4     <NA>
5      zzz
6      aaa
7      bbb
8      bbb
9     <NA>
10    <NA>
11     zzz
Name: a, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_describe[include8-None-percentiles8-None] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = ['number', string[pyarrow]], exclude = None
percentiles = [0.25, 0.75], subset = None

    @pytest.mark.parametrize(
        "include,exclude,percentiles,subset",
        [
            (None, None, None, ["c", "d"]),  # numeric
            (None, None, None, ["c", "d", "f"]),  # numeric + timedelta
            (None, None, None, ["c", "d", "g"]),  # numeric + bool
            (None, None, None, ["c", "d", "f", "g"]),  # numeric + bool + timedelta
            (None, None, None, ["f", "g"]),  # bool + timedelta
            ("all", None, None, None),
            (["number"], None, [0.25, 0.5], None),
            ([np.timedelta64], None, None, None),
            (["number", get_string_dtype()], None, [0.25, 0.75], None),
            (None, ["number", get_string_dtype()], None, None),
            ([get_string_dtype(), "datetime", "bool"], None, None, None),
        ],
    )
    def test_describe(include, exclude, percentiles, subset):
        data = {
            "a": ["aaa", "bbb", "bbb", None, None, "zzz"] * 2,
            "c": [None, 0, 1, 2, 3, 4] * 2,
            "d": [None, 0, 1] * 4,
            "e": [
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 07:56:23.858694"),
                pd.Timestamp("2017-05-09 05:59:58.938999"),
                None,
                None,
            ]
            * 2,
            "f": [
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
                None,
                None,
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
            ]
            * 2,
            "g": [True, False, True] * 4,
        }
    
        # Arrange
        df = pd.DataFrame(data)
>       df["a"] = df["a"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:429: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        a    c    d                          e      f      g
0    aaa  NaN  NaN 2017-05-09 00:00:00.006000 3 days   Tru...10  None  3.0  0.0                        NaT 3 days  False
11   zzz  4.0  1.0                        NaT 1 days   True
key = 'a'
value = 0      aaa
1      bbb
2      bbb
3     <NA>
4     <NA>
5      zzz
6      aaa
7      bbb
8      bbb
9     <NA>
10    <NA>
11     zzz
Name: a, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_describe[None-exclude9-None-None] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = None, exclude = ['number', string[pyarrow]], percentiles = None
subset = None

    @pytest.mark.parametrize(
        "include,exclude,percentiles,subset",
        [
            (None, None, None, ["c", "d"]),  # numeric
            (None, None, None, ["c", "d", "f"]),  # numeric + timedelta
            (None, None, None, ["c", "d", "g"]),  # numeric + bool
            (None, None, None, ["c", "d", "f", "g"]),  # numeric + bool + timedelta
            (None, None, None, ["f", "g"]),  # bool + timedelta
            ("all", None, None, None),
            (["number"], None, [0.25, 0.5], None),
            ([np.timedelta64], None, None, None),
            (["number", get_string_dtype()], None, [0.25, 0.75], None),
            (None, ["number", get_string_dtype()], None, None),
            ([get_string_dtype(), "datetime", "bool"], None, None, None),
        ],
    )
    def test_describe(include, exclude, percentiles, subset):
        data = {
            "a": ["aaa", "bbb", "bbb", None, None, "zzz"] * 2,
            "c": [None, 0, 1, 2, 3, 4] * 2,
            "d": [None, 0, 1] * 4,
            "e": [
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 07:56:23.858694"),
                pd.Timestamp("2017-05-09 05:59:58.938999"),
                None,
                None,
            ]
            * 2,
            "f": [
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
                None,
                None,
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
            ]
            * 2,
            "g": [True, False, True] * 4,
        }
    
        # Arrange
        df = pd.DataFrame(data)
>       df["a"] = df["a"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:429: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        a    c    d                          e      f      g
0    aaa  NaN  NaN 2017-05-09 00:00:00.006000 3 days   Tru...10  None  3.0  0.0                        NaT 3 days  False
11   zzz  4.0  1.0                        NaT 1 days   True
key = 'a'
value = 0      aaa
1      bbb
2      bbb
3     <NA>
4     <NA>
5      zzz
6      aaa
7      bbb
8      bbb
9     <NA>
10    <NA>
11     zzz
Name: a, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_describe[include10-None-None-None] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = [string[pyarrow], 'datetime', 'bool'], exclude = None
percentiles = None, subset = None

    @pytest.mark.parametrize(
        "include,exclude,percentiles,subset",
        [
            (None, None, None, ["c", "d"]),  # numeric
            (None, None, None, ["c", "d", "f"]),  # numeric + timedelta
            (None, None, None, ["c", "d", "g"]),  # numeric + bool
            (None, None, None, ["c", "d", "f", "g"]),  # numeric + bool + timedelta
            (None, None, None, ["f", "g"]),  # bool + timedelta
            ("all", None, None, None),
            (["number"], None, [0.25, 0.5], None),
            ([np.timedelta64], None, None, None),
            (["number", get_string_dtype()], None, [0.25, 0.75], None),
            (None, ["number", get_string_dtype()], None, None),
            ([get_string_dtype(), "datetime", "bool"], None, None, None),
        ],
    )
    def test_describe(include, exclude, percentiles, subset):
        data = {
            "a": ["aaa", "bbb", "bbb", None, None, "zzz"] * 2,
            "c": [None, 0, 1, 2, 3, 4] * 2,
            "d": [None, 0, 1] * 4,
            "e": [
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 00:00:00.006000"),
                pd.Timestamp("2017-05-09 07:56:23.858694"),
                pd.Timestamp("2017-05-09 05:59:58.938999"),
                None,
                None,
            ]
            * 2,
            "f": [
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
                None,
                None,
                np.timedelta64(3, "D"),
                np.timedelta64(1, "D"),
            ]
            * 2,
            "g": [True, False, True] * 4,
        }
    
        # Arrange
        df = pd.DataFrame(data)
>       df["a"] = df["a"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:429: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        a    c    d                          e      f      g
0    aaa  NaN  NaN 2017-05-09 00:00:00.006000 3 days   Tru...10  None  3.0  0.0                        NaT 3 days  False
11   zzz  4.0  1.0                        NaT 1 days   True
key = 'a'
value = 0      aaa
1      bbb
2      bbb
3     <NA>
4     <NA>
5      zzz
6      aaa
7      bbb
8      bbb
9     <NA>
10    <NA>
11     zzz
Name: a, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_groupby_get_group[disk-True-<lambda>0] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

categoricals = True, by = <function <lambda> at 0x7ffb8f89f5e0>

    @pytest.mark.parametrize(
        "categoricals,by",
        [
            (True, lambda df: "b"),
            (False, lambda df: "b"),
            (True, lambda df: df.b),
            (False, lambda df: df.b),
            (False, lambda df: df.b + 1),
        ],
    )
    def test_groupby_get_group(categoricals, by):
        dsk = {
            ("x", 0): pd.DataFrame({"a": [1, 2, 6], "b": [4, 2, 7]}, index=[0, 1, 3]),
            ("x", 1): pd.DataFrame({"a": [4, 2, 6], "b": [3, 3, 1]}, index=[5, 6, 8]),
            ("x", 2): pd.DataFrame({"a": [4, 3, 7], "b": [1, 1, 3]}, index=[9, 9, 9]),
        }
        ddf = dd.repartition(pd.concat(dsk.values()), divisions=[0, 4, 9, 9])
    
        if categoricals:
>           ddf = ddf.categorize(columns=["b"])

dask/dataframe/tests/test_groupby.py:364: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3802: in categorize
    return new_collection(Categorize(self, categories, index))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_categorical.py:175: in _meta
    return _categorize_block(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/categorical.py:25: in _categorize_block
    df[col] = df[col].astype(cat_dtype)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = 'b'
value = Series([], Name: b, dtype: category
Categories (5, int64): [1, 2, 3, 4, 7])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_groupby_get_group[disk-True-<lambda>1] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

categoricals = True, by = <function <lambda> at 0x7ffb8f89f740>

    @pytest.mark.parametrize(
        "categoricals,by",
        [
            (True, lambda df: "b"),
            (False, lambda df: "b"),
            (True, lambda df: df.b),
            (False, lambda df: df.b),
            (False, lambda df: df.b + 1),
        ],
    )
    def test_groupby_get_group(categoricals, by):
        dsk = {
            ("x", 0): pd.DataFrame({"a": [1, 2, 6], "b": [4, 2, 7]}, index=[0, 1, 3]),
            ("x", 1): pd.DataFrame({"a": [4, 2, 6], "b": [3, 3, 1]}, index=[5, 6, 8]),
            ("x", 2): pd.DataFrame({"a": [4, 3, 7], "b": [1, 1, 3]}, index=[9, 9, 9]),
        }
        ddf = dd.repartition(pd.concat(dsk.values()), divisions=[0, 4, 9, 9])
    
        if categoricals:
>           ddf = ddf.categorize(columns=["b"])

dask/dataframe/tests/test_groupby.py:364: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3802: in categorize
    return new_collection(Categorize(self, categories, index))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_categorical.py:175: in _meta
    return _categorize_block(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/categorical.py:25: in _categorize_block
    df[col] = df[col].astype(cat_dtype)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = 'b'
value = Series([], Name: b, dtype: category
Categories (5, int64): [1, 2, 3, 4, 7])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_groupby_get_group[tasks-True-<lambda>0] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

categoricals = True, by = <function <lambda> at 0x7ffb8f89f5e0>

    @pytest.mark.parametrize(
        "categoricals,by",
        [
            (True, lambda df: "b"),
            (False, lambda df: "b"),
            (True, lambda df: df.b),
            (False, lambda df: df.b),
            (False, lambda df: df.b + 1),
        ],
    )
    def test_groupby_get_group(categoricals, by):
        dsk = {
            ("x", 0): pd.DataFrame({"a": [1, 2, 6], "b": [4, 2, 7]}, index=[0, 1, 3]),
            ("x", 1): pd.DataFrame({"a": [4, 2, 6], "b": [3, 3, 1]}, index=[5, 6, 8]),
            ("x", 2): pd.DataFrame({"a": [4, 3, 7], "b": [1, 1, 3]}, index=[9, 9, 9]),
        }
        ddf = dd.repartition(pd.concat(dsk.values()), divisions=[0, 4, 9, 9])
    
        if categoricals:
>           ddf = ddf.categorize(columns=["b"])

dask/dataframe/tests/test_groupby.py:364: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3802: in categorize
    return new_collection(Categorize(self, categories, index))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_categorical.py:175: in _meta
    return _categorize_block(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/categorical.py:25: in _categorize_block
    df[col] = df[col].astype(cat_dtype)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = 'b'
value = Series([], Name: b, dtype: category
Categories (5, int64): [1, 2, 3, 4, 7])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_groupby_get_group[tasks-True-<lambda>1] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

categoricals = True, by = <function <lambda> at 0x7ffb8f89f740>

    @pytest.mark.parametrize(
        "categoricals,by",
        [
            (True, lambda df: "b"),
            (False, lambda df: "b"),
            (True, lambda df: df.b),
            (False, lambda df: df.b),
            (False, lambda df: df.b + 1),
        ],
    )
    def test_groupby_get_group(categoricals, by):
        dsk = {
            ("x", 0): pd.DataFrame({"a": [1, 2, 6], "b": [4, 2, 7]}, index=[0, 1, 3]),
            ("x", 1): pd.DataFrame({"a": [4, 2, 6], "b": [3, 3, 1]}, index=[5, 6, 8]),
            ("x", 2): pd.DataFrame({"a": [4, 3, 7], "b": [1, 1, 3]}, index=[9, 9, 9]),
        }
        ddf = dd.repartition(pd.concat(dsk.values()), divisions=[0, 4, 9, 9])
    
        if categoricals:
>           ddf = ddf.categorize(columns=["b"])

dask/dataframe/tests/test_groupby.py:364: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3802: in categorize
    return new_collection(Categorize(self, categories, index))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_categorical.py:175: in _meta
    return _categorize_block(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/categorical.py:25: in _categorize_block
    df[col] = df[col].astype(cat_dtype)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = 'b'
value = Series([], Name: b, dtype: category
Categories (5, int64): [1, 2, 3, 4, 7])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_groupby_cumfunc_with_named_index[disk-cumsum-1] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, func = 'cumsum'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:679: in compute
    keys = list(flatten(expr.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:1176: in __dask_keys__
    all_keys.append(list(op.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:94: in __dask_keys__
    return [(self._name, i) for i in range(self.npartitions)]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:1453: in _divisions
    return self.frame.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_groupby_cumfunc_with_named_index[disk-cumsum-2] __________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 2, func = 'cumsum'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_groupby_cumfunc_with_named_index[disk-cumprod-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, func = 'cumprod'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:679: in compute
    keys = list(flatten(expr.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:1176: in __dask_keys__
    all_keys.append(list(op.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:94: in __dask_keys__
    return [(self._name, i) for i in range(self.npartitions)]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:1453: in _divisions
    return self.frame.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_groupby_cumfunc_with_named_index[disk-cumprod-2] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 2, func = 'cumprod'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_groupby_cumfunc_with_named_index[disk-cumcount-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, func = 'cumcount'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:679: in compute
    keys = list(flatten(expr.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:1176: in __dask_keys__
    all_keys.append(list(op.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:94: in __dask_keys__
    return [(self._name, i) for i in range(self.npartitions)]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:1453: in _divisions
    return self.frame.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_groupby_cumfunc_with_named_index[disk-cumcount-2] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 2, func = 'cumcount'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_groupby_cumfunc_with_named_index[tasks-cumsum-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, func = 'cumsum'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:679: in compute
    keys = list(flatten(expr.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:1176: in __dask_keys__
    all_keys.append(list(op.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:94: in __dask_keys__
    return [(self._name, i) for i in range(self.npartitions)]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:1453: in _divisions
    return self.frame.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_groupby_cumfunc_with_named_index[tasks-cumsum-2] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 2, func = 'cumsum'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_groupby_cumfunc_with_named_index[tasks-cumprod-1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, func = 'cumprod'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:679: in compute
    keys = list(flatten(expr.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:1176: in __dask_keys__
    all_keys.append(list(op.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:94: in __dask_keys__
    return [(self._name, i) for i in range(self.npartitions)]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:1453: in _divisions
    return self.frame.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_groupby_cumfunc_with_named_index[tasks-cumprod-2] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 2, func = 'cumprod'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [y]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_groupby_cumfunc_with_named_index[tasks-cumcount-1] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 1, func = 'cumcount'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:679: in compute
    keys = list(flatten(expr.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:1176: in __dask_keys__
    all_keys.append(list(op.__dask_keys__()))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:94: in __dask_keys__
    return [(self._name, i) for i in range(self.npartitions)]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:446: in npartitions
    return len(self.divisions) - 1
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:1453: in _divisions
    return self.frame.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:594: in _divisions
    if not self._broadcast_dep(arg):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:585: in _broadcast_dep
    return dep.npartitions == 1 and dep.ndim < self.ndim
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:87: in ndim
    meta = self._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________________ test_assign __________________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_assign():
        df = pd.DataFrame(
            {"a": range(8), "b": [float(i) for i in range(10, 18)]},
            index=pd.Index(list("abcdefgh")),
        )
        ddf = dd.from_pandas(df, npartitions=3)
        ddf_unknown = dd.from_pandas(df, npartitions=3, sort=False).clear_divisions()
        assert not ddf_unknown.known_divisions
    
>       res = ddf.assign(
            c=1,
            d="string",
            e=ddf.a.sum(),
            f=ddf.a + ddf.b,
            g=lambda x: x.a + x.c,
            dt=pd.Timestamp(2018, 2, 13),
        )

dask/dataframe/tests/test_dataframe.py:1570: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2803: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = 'c', value = 1

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_groupby_cumfunc_with_named_index[tasks-cumcount-2] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 2, func = 'cumcount'

    @pytest.mark.parametrize("npartitions", (1, 2))
    @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount"))
    def test_series_groupby_cumfunc_with_named_index(npartitions, func):
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]}
        ).set_index("x")
        ddf = dd.from_pandas(df, npartitions)
        assert ddf.npartitions == npartitions
        expected = getattr(df["y"].groupby("x"), func)()
        result = getattr(ddf["y"].groupby("x"), func)()
>       assert_eq(result, expected)

dask/dataframe/tests/test_groupby.py:425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_x'
value = Index([], dtype='int64', name='x')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________________ test_assign_callable _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_assign_callable():
        df = dd.from_pandas(pd.DataFrame({"A": range(10)}), npartitions=2)
>       a = df.assign(B=df.A.shift())

dask/dataframe/tests/test_dataframe.py:1623: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A]
Index: [], key = 'B'
value = Series([], Name: A, dtype: float64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________________ test_assign_dtypes ______________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_assign_dtypes():
        ddf = dd.from_pandas(
            pd.DataFrame(
                data={"col1": ["a", "b"], "col2": [1, 2]}, columns=["col1", "col2"]
            ),
            npartitions=2,
        )
    
        new_col = {"col3": pd.Series(["0", "1"])}
>       res = ddf.assign(**new_col)

dask/dataframe/tests/test_dataframe.py:1637: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2814: in assign
    result = new_collection(expr.AssignAlign(result, k, v.expr))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:3520: in _meta
    return self._expr_cls(*self.operands)._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [col1, col2]
Index: [], key = 'col3'
value = Series([], dtype: string)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_assign_pandas_series ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_assign_pandas_series():
        # Make sure we handle when `df.columns.min()` raises TypeError
        df = pd.DataFrame({"a": [1, 2], 1: [5, 6]})
        ddf = dd.from_pandas(df, npartitions=2)
>       ddf = ddf.assign(c=df["a"])

dask/dataframe/tests/test_dataframe.py:1653: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2814: in assign
    result = new_collection(expr.AssignAlign(result, k, v.expr))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:3520: in _meta
    return self._expr_cls(*self.operands)._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, 1]
Index: [], key = 'c'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_fillna_duplicate_index __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_fillna_duplicate_index():
        @dask.delayed
        def f():
            return pd.DataFrame(dict(a=[1.0], b=[np.nan]))
    
        ddf = dd.from_delayed([f(), f()], meta=dict(a=float, b=float))
>       ddf.b = ddf.b.fillna(ddf.a)

dask/dataframe/tests/test_dataframe.py:2444: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3073: in __setattr__
    self[key] = value
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = 'b'
value = Series([], Name: b, dtype: float64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_fillna_series_types ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_fillna_series_types():
        # https://github.com/dask/dask/issues/2809
        df = pd.DataFrame({"A": [1, np.nan, 3], "B": [1, np.nan, 3]})
        ddf = dd.from_pandas(df, npartitions=2)
        fill_value = pd.Series([1, 10], index=["A", "C"])
>       assert_eq(ddf.fillna(fill_value), df.fillna(fill_value))

dask/dataframe/tests/test_dataframe.py:2483: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2006: in fillna
    return new_collection(self.expr.fillna(value))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:561: in _meta
    return self.operation(*args, **self._kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/utils.py:1226: in __call__
    return getattr(__obj, self.method)(*args, **kwargs)
/usr/lib64/python3.14/site-packages/pandas/core/generic.py:7387: in fillna
    result[k] = res_k
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = 'A'
value = Series([], Name: A, dtype: float64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_select_dtypes[include0-None] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = [<class 'int'>], exclude = None

    @pytest.mark.parametrize(
        "include, exclude",
        [
            ([int], None),
            (None, [int]),
            ([np.number, get_string_dtype()], [float]),
            (["datetime"], None),
        ],
    )
    def test_select_dtypes(include, exclude):
        n = 10
        df = pd.DataFrame(
            {
                "cint": [1] * n,
                "cstr": ["a"] * n,
                "clfoat": [1.0] * n,
                "cdt": pd.date_range("2016-01-01", periods=n),
            }
        )
>       df["cstr"] = df["cstr"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:2568: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    cint cstr  clfoat        cdt
0     1    a     1.0 2016-01-01
1     1    a     1.0 2016-01-02
2     1    a     1.0 2...   a     1.0 2016-01-07
7     1    a     1.0 2016-01-08
8     1    a     1.0 2016-01-09
9     1    a     1.0 2016-01-10
key = 'cstr'
value = 0    a
1    a
2    a
3    a
4    a
5    a
6    a
7    a
8    a
9    a
Name: cstr, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_select_dtypes[None-exclude1] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = None, exclude = [<class 'int'>]

    @pytest.mark.parametrize(
        "include, exclude",
        [
            ([int], None),
            (None, [int]),
            ([np.number, get_string_dtype()], [float]),
            (["datetime"], None),
        ],
    )
    def test_select_dtypes(include, exclude):
        n = 10
        df = pd.DataFrame(
            {
                "cint": [1] * n,
                "cstr": ["a"] * n,
                "clfoat": [1.0] * n,
                "cdt": pd.date_range("2016-01-01", periods=n),
            }
        )
>       df["cstr"] = df["cstr"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:2568: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    cint cstr  clfoat        cdt
0     1    a     1.0 2016-01-01
1     1    a     1.0 2016-01-02
2     1    a     1.0 2...   a     1.0 2016-01-07
7     1    a     1.0 2016-01-08
8     1    a     1.0 2016-01-09
9     1    a     1.0 2016-01-10
key = 'cstr'
value = 0    a
1    a
2    a
3    a
4    a
5    a
6    a
7    a
8    a
9    a
Name: cstr, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_select_dtypes[include2-exclude2] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = [<class 'numpy.number'>, string[pyarrow]], exclude = [<class 'float'>]

    @pytest.mark.parametrize(
        "include, exclude",
        [
            ([int], None),
            (None, [int]),
            ([np.number, get_string_dtype()], [float]),
            (["datetime"], None),
        ],
    )
    def test_select_dtypes(include, exclude):
        n = 10
        df = pd.DataFrame(
            {
                "cint": [1] * n,
                "cstr": ["a"] * n,
                "clfoat": [1.0] * n,
                "cdt": pd.date_range("2016-01-01", periods=n),
            }
        )
>       df["cstr"] = df["cstr"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:2568: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    cint cstr  clfoat        cdt
0     1    a     1.0 2016-01-01
1     1    a     1.0 2016-01-02
2     1    a     1.0 2...   a     1.0 2016-01-07
7     1    a     1.0 2016-01-08
8     1    a     1.0 2016-01-09
9     1    a     1.0 2016-01-10
key = 'cstr'
value = 0    a
1    a
2    a
3    a
4    a
5    a
6    a
7    a
8    a
9    a
Name: cstr, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_select_dtypes[include3-None] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

include = ['datetime'], exclude = None

    @pytest.mark.parametrize(
        "include, exclude",
        [
            ([int], None),
            (None, [int]),
            ([np.number, get_string_dtype()], [float]),
            (["datetime"], None),
        ],
    )
    def test_select_dtypes(include, exclude):
        n = 10
        df = pd.DataFrame(
            {
                "cint": [1] * n,
                "cstr": ["a"] * n,
                "clfoat": [1.0] * n,
                "cdt": pd.date_range("2016-01-01", periods=n),
            }
        )
>       df["cstr"] = df["cstr"].astype(get_string_dtype())

dask/dataframe/tests/test_dataframe.py:2568: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    cint cstr  clfoat        cdt
0     1    a     1.0 2016-01-01
1     1    a     1.0 2016-01-02
2     1    a     1.0 2...   a     1.0 2016-01-07
7     1    a     1.0 2016-01-08
8     1    a     1.0 2016-01-09
9     1    a     1.0 2016-01-10
key = 'cstr'
value = 0    a
1    a
2    a
3    a
4    a
5    a
6    a
7    a
8    a
9    a
Name: cstr, dtype: string

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_drop_columns[columns0] __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

columns = ['b']

    @pytest.mark.parametrize("columns", [["b"], []])
    def test_drop_columns(columns):
        # Check both populated and empty list argument
        # https://github.com/dask/dask/issues/6870
    
        df = pd.DataFrame(
            {
                "a": [2, 4, 6, 8],
                "b": ["1a", "2b", "3c", "4d"],
            }
        )
        ddf = dd.from_pandas(df, npartitions=2)
        ddf2 = ddf.drop(columns=columns)
>       ddf["new"] = ddf["a"] + 1  # Check that ddf2 is not modified

dask/dataframe/tests/test_dataframe.py:2731: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = 'new'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_drop_columns[columns1] __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

columns = []

    @pytest.mark.parametrize("columns", [["b"], []])
    def test_drop_columns(columns):
        # Check both populated and empty list argument
        # https://github.com/dask/dask/issues/6870
    
        df = pd.DataFrame(
            {
                "a": [2, 4, 6, 8],
                "b": ["1a", "2b", "3c", "4d"],
            }
        )
        ddf = dd.from_pandas(df, npartitions=2)
        ddf2 = ddf.drop(columns=columns)
>       ddf["new"] = ddf["a"] + 1  # Check that ddf2 is not modified

dask/dataframe/tests/test_dataframe.py:2731: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = 'new'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_cov_corr_mixed[True] ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

numeric_only = True

    @pytest.mark.parametrize(
        "numeric_only",
        [
            pytest.param(
                None,
                marks=pytest.mark.xfail(reason="fails with non-numeric data"),
            ),
            pytest.param(
                True,
            ),
            pytest.param(
                False,
                marks=[
                    pytest.mark.xfail(reason="fails with non-numeric data"),
                ],
            ),
        ],
    )
    def test_cov_corr_mixed(numeric_only):
        size = 1000
        d = {
            "dates": pd.date_range("2015-01-01", periods=size, freq="1min"),
            "unique_id": np.arange(0, size),
            "ints": np.random.randint(0, size, size=size),
            "floats": np.random.randn(size),
            "bools": np.random.choice([0, 1], size=size),
            "int_nans": np.random.choice([0, 1, np.nan], size=size),
            "float_nans": np.random.choice([0.0, 1.0, np.nan], size=size),
            "constant": 1,
            "int_categorical": np.random.choice([10, 20, 30, 40, 50], size=size),
            "categorical_binary": np.random.choice(["a", "b"], size=size),
            "categorical_nans": np.random.choice(["a", "b", "c"], size=size),
        }
        df = pd.DataFrame(d)
>       df["hardbools"] = df["bools"] == 1

dask/dataframe/tests/test_dataframe.py:3198: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =                   dates  unique_id  ...  categorical_binary  categorical_nans
0   2015-01-01 00:00:00          0  ... ...              b
999 2015-01-01 16:39:00        999  ...                   b                 b

[1000 rows x 11 columns]
key = 'hardbools'
value = 0      False
1       True
2      False
3      False
4      False
       ...  
995     True
996     True
997    False
998    False
999     True
Name: bools, Length: 1000, dtype: bool

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_astype_categoricals ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_astype_categoricals():
        df = pd.DataFrame(
            {
                "x": ["a", "b", "c", "b", "c"],
                "y": ["x", "y", "z", "x", "y"],
                "z": [1, 2, 3, 4, 5],
            }
        )
        df = df.astype({"y": "category"})
        ddf = dd.from_pandas(df, 2)
        assert ddf.y.cat.known
    
>       ddf2 = ddf.astype({"x": "category"})

dask/dataframe/tests/test_dataframe.py:3436: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1902: in astype
    return new_collection(self.expr.astype(dtypes))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1573: in _meta
    meta = clear_known_categories(meta, cols=set_unknown)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:250: in clear_known_categories
    x[c] = x[c].cat.set_categories([UNKNOWN_CATEGORIES])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = 'x'
value = Series([], Name: x, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_astype_categoricals_known ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_astype_categoricals_known():
        df = pd.DataFrame(
            {
                "x": ["a", "b", "c", "b", "c"],
                "y": ["x", "y", "z", "y", "z"],
                "z": ["b", "b", "b", "c", "b"],
                "other": [1, 2, 3, 4, 5],
            }
        )
        ddf = dd.from_pandas(df, 2)
    
        abc = pd.api.types.CategoricalDtype(["a", "b", "c"], ordered=False)
        category = pd.api.types.CategoricalDtype(ordered=False)
    
        # DataFrame
>       ddf2 = ddf.astype({"x": abc, "y": category, "z": "category", "other": "f8"})

dask/dataframe/tests/test_dataframe.py:3463: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1902: in astype
    return new_collection(self.expr.astype(dtypes))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1573: in _meta
    meta = clear_known_categories(meta, cols=set_unknown)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:250: in clear_known_categories
    x[c] = x[c].cat.set_categories([UNKNOWN_CATEGORIES])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z, other]
Index: [], key = 'y'
value = Series([], Name: y, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________________ test_gh_1301 _________________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_gh_1301():
        df = pd.DataFrame([["1", "2"], ["3", "4"]])
        ddf = dd.from_pandas(df, npartitions=2)
>       ddf2 = ddf.assign(y=ddf[1].astype(int))

dask/dataframe/tests/test_dataframe.py:3518: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0, 1]
Index: [], key = 'y'
value = Series([], Name: 1, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_column_assignment ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_column_assignment():
        df = pd.DataFrame({"x": [1, 2, 3, 4], "y": [1, 0, 1, 0]})
        ddf = dd.from_pandas(df, npartitions=2)
        orig = ddf.copy()
>       ddf["z"] = ddf.x + ddf.y

dask/dataframe/tests/test_dataframe.py:3574: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = 'z'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_array_assignment _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_array_assignment():
        df = pd.DataFrame({"x": np.random.normal(size=50), "y": np.random.normal(size=50)})
        ddf = dd.from_pandas(df, npartitions=2)
        orig = ddf.copy()
    
        arr = np.array(np.random.normal(size=50))
        darr = da.from_array(arr, chunks=25)
    
        df["z"] = arr
>       ddf["z"] = darr

dask/dataframe/tests/test_dataframe.py:3590: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = 'z'
value = Series([], dtype: float64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_columns_assignment ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_columns_assignment():
        df = pd.DataFrame({"x": [1, 2, 3, 4]})
        ddf = dd.from_pandas(df, npartitions=2)
    
        df2 = df.assign(y=df.x + 1, z=df.x - 1)
        df[["a", "b"]] = df2[["y", "z"]]
    
>       ddf2 = ddf.assign(y=ddf.x + 1, z=ddf.x - 1)

dask/dataframe/tests/test_dataframe.py:3614: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'y'
value = Series([], Name: x, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_attribute_assignment ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_attribute_assignment():
        df = pd.DataFrame({"x": [1, 2, 3, 4, 5], "y": [1.0, 2.0, 3.0, 4.0, 5.0]})
        ddf = dd.from_pandas(df, npartitions=2)
    
>       ddf.y = ddf.x + ddf.y

dask/dataframe/tests/test_dataframe.py:3624: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3073: in __setattr__
    self[key] = value
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = 'y'
value = Series([], dtype: float64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_setitem_triggering_realign ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_setitem_triggering_realign():
        a = dd.from_pandas(pd.DataFrame({"A": range(12)}), npartitions=3)
        b = dd.from_pandas(pd.Series(range(12), name="B"), npartitions=4)
>       a["C"] = b

dask/dataframe/tests/test_dataframe.py:3631: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2814: in assign
    result = new_collection(expr.AssignAlign(result, k, v.expr))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:3520: in _meta
    return self._expr_cls(*self.operands)._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A]
Index: [], key = 'C'
value = Series([], Name: B, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_inplace_operators ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_inplace_operators():
        df = pd.DataFrame({"x": [1, 2, 3, 4, 5], "y": [1.0, 2.0, 3.0, 4.0, 5.0]})
        ddf = dd.from_pandas(df, npartitions=2)
    
>       ddf["y"] **= 0.5

dask/dataframe/tests/test_dataframe.py:3639: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: float64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________________ test_copy ___________________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_copy():
        df = pd.DataFrame({"x": [1, 2, 3]})
    
        a = dd.from_pandas(df, npartitions=2)
        b = a.copy()
        c = a.copy(deep=False)
    
>       a["y"] = a.x * 2

dask/dataframe/tests/test_dataframe.py:4079: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = 'y'
value = Series([], Name: x, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_groupby_reduction_split[median-disk-split_every] _____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

keyword = 'split_every', agg_func = 'median', shuffle_method = 'disk'

    @pytest.mark.parametrize("keyword", ["split_every", "split_out"])
    def test_groupby_reduction_split(keyword, agg_func, shuffle_method):
        if agg_func in {"first", "last"} and shuffle_method == "disk":
            pytest.skip(reason="https://github.com/dask/dask/issues/10034")
        pdf = pd.DataFrame(
            {"a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 100, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 100}
        )
        ddf = dd.from_pandas(pdf, npartitions=15)
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # DataFrame
        # nunique is not implemented for DataFrameGroupBy
        # covariance/correlation is not a series aggregation
        if agg_func not in ("nunique", "cov", "corr"):
            res = call(ddf.groupby("b", sort=False), agg_func, **{keyword: 2})
            sol = call(pdf.groupby("b"), agg_func)
            assert_eq(res, sol)
            assert call(ddf.groupby("b"), agg_func)._name != res._name
    
        if agg_func == "var":
            res = call(ddf.groupby("b", sort=False), "var", ddof=2, **{keyword: 2})
            sol = call(pdf.groupby("b"), "var", ddof=2)
            assert_eq(res, sol)
            assert call(ddf.groupby("b"), "var", ddof=2)._name != res._name
    
        # Series, post select
        # covariance/correlation is not a series aggregation
        if agg_func not in ("cov", "corr"):
            res = call(ddf.groupby("b", sort=False).a, agg_func, **{keyword: 2})
            sol = call(pdf.groupby("b").a, agg_func)
            assert_eq(res, sol)
            assert call(ddf.groupby("b").a, agg_func)._name != res._name
    
        if agg_func == "var":
            res = call(ddf.groupby("b", sort=False).a, "var", ddof=2, **{keyword: 2})
            sol = call(pdf.groupby("b").a, "var", ddof=2)
            assert_eq(res, sol)
            assert call(ddf.groupby("b").a, "var", ddof=2)._name != res._name
    
        # Series, pre select
        # covariance/correlation is not a series aggregation
        if agg_func not in ("cov", "corr"):
            res = call(ddf.a.groupby(ddf.b, sort=False), agg_func, **{keyword: 2})
            sol = call(pdf.a.groupby(pdf.b), agg_func)
            # There's a bug in pandas 0.18.0 with `pdf.a.groupby(pdf.b).count()`
            # not forwarding the series name. Skip name checks here for now.
>           assert_eq(res, sol, check_names=False)

dask/dataframe/tests/test_groupby.py:734: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_0'
value = Series([], Name: b, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_groupby_reduction_split[median-disk-split_out] ______________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

keyword = 'split_out', agg_func = 'median', shuffle_method = 'disk'

    @pytest.mark.parametrize("keyword", ["split_every", "split_out"])
    def test_groupby_reduction_split(keyword, agg_func, shuffle_method):
        if agg_func in {"first", "last"} and shuffle_method == "disk":
            pytest.skip(reason="https://github.com/dask/dask/issues/10034")
        pdf = pd.DataFrame(
            {"a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 100, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 100}
        )
        ddf = dd.from_pandas(pdf, npartitions=15)
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # DataFrame
        # nunique is not implemented for DataFrameGroupBy
        # covariance/correlation is not a series aggregation
        if agg_func not in ("nunique", "cov", "corr"):
            res = call(ddf.groupby("b", sort=False), agg_func, **{keyword: 2})
            sol = call(pdf.groupby("b"), agg_func)
            assert_eq(res, sol)
            assert call(ddf.groupby("b"), agg_func)._name != res._name
    
        if agg_func == "var":
            res = call(ddf.groupby("b", sort=False), "var", ddof=2, **{keyword: 2})
            sol = call(pdf.groupby("b"), "var", ddof=2)
            assert_eq(res, sol)
            assert call(ddf.groupby("b"), "var", ddof=2)._name != res._name
    
        # Series, post select
        # covariance/correlation is not a series aggregation
        if agg_func not in ("cov", "corr"):
            res = call(ddf.groupby("b", sort=False).a, agg_func, **{keyword: 2})
            sol = call(pdf.groupby("b").a, agg_func)
            assert_eq(res, sol)
            assert call(ddf.groupby("b").a, agg_func)._name != res._name
    
        if agg_func == "var":
            res = call(ddf.groupby("b", sort=False).a, "var", ddof=2, **{keyword: 2})
            sol = call(pdf.groupby("b").a, "var", ddof=2)
            assert_eq(res, sol)
            assert call(ddf.groupby("b").a, "var", ddof=2)._name != res._name
    
        # Series, pre select
        # covariance/correlation is not a series aggregation
        if agg_func not in ("cov", "corr"):
            res = call(ddf.a.groupby(ddf.b, sort=False), agg_func, **{keyword: 2})
            sol = call(pdf.a.groupby(pdf.b), agg_func)
            # There's a bug in pandas 0.18.0 with `pdf.a.groupby(pdf.b).count()`
            # not forwarding the series name. Skip name checks here for now.
>           assert_eq(res, sol, check_names=False)

dask/dataframe/tests/test_groupby.py:734: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_0'
value = Series([], Name: b, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative_multiple_columns _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_cumulative_multiple_columns():
        # GH 3037
        df = pd.DataFrame(np.random.randn(100, 5), columns=list("abcde"))
        ddf = dd.from_pandas(df, 5)
    
        for d in [ddf, df]:
            for c in df.columns:
>               d[c + "cs"] = d[c].cumsum()

dask/dataframe/tests/test_dataframe.py:4460: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b, c, d, e]
Index: [], key = 'acs'
value = Series([], Name: a, dtype: float64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_groupby_reduction_split[median-tasks-split_every] ____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

keyword = 'split_every', agg_func = 'median', shuffle_method = 'tasks'

    @pytest.mark.parametrize("keyword", ["split_every", "split_out"])
    def test_groupby_reduction_split(keyword, agg_func, shuffle_method):
        if agg_func in {"first", "last"} and shuffle_method == "disk":
            pytest.skip(reason="https://github.com/dask/dask/issues/10034")
        pdf = pd.DataFrame(
            {"a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 100, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 100}
        )
        ddf = dd.from_pandas(pdf, npartitions=15)
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # DataFrame
        # nunique is not implemented for DataFrameGroupBy
        # covariance/correlation is not a series aggregation
        if agg_func not in ("nunique", "cov", "corr"):
            res = call(ddf.groupby("b", sort=False), agg_func, **{keyword: 2})
            sol = call(pdf.groupby("b"), agg_func)
            assert_eq(res, sol)
            assert call(ddf.groupby("b"), agg_func)._name != res._name
    
        if agg_func == "var":
            res = call(ddf.groupby("b", sort=False), "var", ddof=2, **{keyword: 2})
            sol = call(pdf.groupby("b"), "var", ddof=2)
            assert_eq(res, sol)
            assert call(ddf.groupby("b"), "var", ddof=2)._name != res._name
    
        # Series, post select
        # covariance/correlation is not a series aggregation
        if agg_func not in ("cov", "corr"):
            res = call(ddf.groupby("b", sort=False).a, agg_func, **{keyword: 2})
            sol = call(pdf.groupby("b").a, agg_func)
            assert_eq(res, sol)
            assert call(ddf.groupby("b").a, agg_func)._name != res._name
    
        if agg_func == "var":
            res = call(ddf.groupby("b", sort=False).a, "var", ddof=2, **{keyword: 2})
            sol = call(pdf.groupby("b").a, "var", ddof=2)
            assert_eq(res, sol)
            assert call(ddf.groupby("b").a, "var", ddof=2)._name != res._name
    
        # Series, pre select
        # covariance/correlation is not a series aggregation
        if agg_func not in ("cov", "corr"):
            res = call(ddf.a.groupby(ddf.b, sort=False), agg_func, **{keyword: 2})
            sol = call(pdf.a.groupby(pdf.b), agg_func)
            # There's a bug in pandas 0.18.0 with `pdf.a.groupby(pdf.b).count()`
            # not forwarding the series name. Skip name checks here for now.
>           assert_eq(res, sol, check_names=False)

dask/dataframe/tests/test_groupby.py:734: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_0'
value = Series([], Name: b, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________________ test_setitem _________________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_setitem():
        df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
        ddf = dd.from_pandas(df.copy(), 2)
        df[df.columns] = 1
>       ddf[ddf.columns] = 1

dask/dataframe/tests/test_dataframe.py:4645: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3041: in __setitem__
    out = self.assign(**{k: value for k in list(key)})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = 'A', value = 1

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_groupby_reduction_split[median-tasks-split_out] _____________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

keyword = 'split_out', agg_func = 'median', shuffle_method = 'tasks'

    @pytest.mark.parametrize("keyword", ["split_every", "split_out"])
    def test_groupby_reduction_split(keyword, agg_func, shuffle_method):
        if agg_func in {"first", "last"} and shuffle_method == "disk":
            pytest.skip(reason="https://github.com/dask/dask/issues/10034")
        pdf = pd.DataFrame(
            {"a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 100, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 100}
        )
        ddf = dd.from_pandas(pdf, npartitions=15)
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # DataFrame
        # nunique is not implemented for DataFrameGroupBy
        # covariance/correlation is not a series aggregation
        if agg_func not in ("nunique", "cov", "corr"):
            res = call(ddf.groupby("b", sort=False), agg_func, **{keyword: 2})
            sol = call(pdf.groupby("b"), agg_func)
            assert_eq(res, sol)
            assert call(ddf.groupby("b"), agg_func)._name != res._name
    
        if agg_func == "var":
            res = call(ddf.groupby("b", sort=False), "var", ddof=2, **{keyword: 2})
            sol = call(pdf.groupby("b"), "var", ddof=2)
            assert_eq(res, sol)
            assert call(ddf.groupby("b"), "var", ddof=2)._name != res._name
    
        # Series, post select
        # covariance/correlation is not a series aggregation
        if agg_func not in ("cov", "corr"):
            res = call(ddf.groupby("b", sort=False).a, agg_func, **{keyword: 2})
            sol = call(pdf.groupby("b").a, agg_func)
            assert_eq(res, sol)
            assert call(ddf.groupby("b").a, agg_func)._name != res._name
    
        if agg_func == "var":
            res = call(ddf.groupby("b", sort=False).a, "var", ddof=2, **{keyword: 2})
            sol = call(pdf.groupby("b").a, "var", ddof=2)
            assert_eq(res, sol)
            assert call(ddf.groupby("b").a, "var", ddof=2)._name != res._name
    
        # Series, pre select
        # covariance/correlation is not a series aggregation
        if agg_func not in ("cov", "corr"):
            res = call(ddf.a.groupby(ddf.b, sort=False), agg_func, **{keyword: 2})
            sol = call(pdf.a.groupby(pdf.b), agg_func)
            # There's a bug in pandas 0.18.0 with `pdf.a.groupby(pdf.b).count()`
            # not forwarding the series name. Skip name checks here for now.
>           assert_eq(res, sol, check_names=False)

dask/dataframe/tests/test_groupby.py:734: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_0'
value = Series([], Name: b, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_setitem_with_bool_series_as_key _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_setitem_with_bool_series_as_key():
        df = pd.DataFrame({"A": [1, 4], "B": [3, 2]})
        ddf = dd.from_pandas(df.copy(), 2)
>       df[df["A"] > 2] = 5

dask/dataframe/tests/test_dataframe.py:4660: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    A  B
0  1  3
1  4  2, key = 0    False
1     True
Name: A, dtype: bool
value = 5

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________________ test_dtype_cast ________________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_dtype_cast():
        df = pd.DataFrame(
            {
                "A": np.arange(10, dtype=np.int32),
                "B": np.arange(10, dtype=np.int64),
                "C": np.arange(10, dtype=np.float32),
            }
        )
        ddf = dd.from_pandas(df, npartitions=2)
        assert ddf.A.dtype == np.int32
        assert ddf.B.dtype == np.int64
        assert ddf.C.dtype == np.float32
    
        col = pd.Series(np.arange(10, dtype=np.float32)) / 2
        assert col.dtype == np.float32
    
>       ddf = ddf.assign(D=col)

dask/dataframe/tests/test_dataframe.py:4771: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2814: in assign
    result = new_collection(expr.AssignAlign(result, k, v.expr))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:3520: in _meta
    return self._expr_cls(*self.operands)._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B, C]
Index: [], key = 'D'
value = Series([], dtype: float32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_assign_na_float_columns _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_assign_na_float_columns():
        # See https://github.com/dask/dask/issues/7156
        df_pandas = pd.DataFrame({"a": [1.1]}, dtype="Float64")
        df = dd.from_pandas(df_pandas, npartitions=1)
    
>       df = df.assign(new_col=df["a"])

dask/dataframe/tests/test_dataframe.py:4910: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = 'new_col'
value = Series([], Name: a, dtype: Float64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_assign_no_warning_fragmented _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_assign_no_warning_fragmented():
        df = pd.DataFrame({"a": [1, 2, 3, 4, 5] * 10})
        df = dd.from_pandas(df, npartitions=50)
        with warnings.catch_warnings(record=True) as w:
            for i in range(105):
>               df[str(i)] = 5

dask/dataframe/tests/test_dataframe.py:4921: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '0', value = 5

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________________ test_dot ___________________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_dot():
        s1 = pd.Series([1, 2, 3, 4])
        s2 = pd.Series([4, 5, 6, 6])
        df = pd.DataFrame({"one": s1, "two": s2})
    
        dask_s1 = dd.from_pandas(s1, npartitions=1)
        dask_df = dd.from_pandas(df, npartitions=1)
        dask_s2 = dd.from_pandas(s2, npartitions=1)
    
        assert_eq(s1.dot(s2), dask_s1.dot(dask_s2))
>       assert_eq(s1.dot(df), dask_s1.dot(dask_df))

dask/dataframe/tests/test_dataframe.py:4935: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_0'
value = Index([], dtype='object')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________________ test_dot_nan _________________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_dot_nan():
        # Test that nan inputs match pandas' behavior
        s1 = pd.Series([1, 2, 3, 4])
        dask_s1 = dd.from_pandas(s1, npartitions=1)
    
        s2 = pd.Series([np.nan, np.nan, np.nan, np.nan])
        dask_s2 = dd.from_pandas(s2, npartitions=1)
    
        df = pd.DataFrame({"one": s1, "two": s2})
        dask_df = dd.from_pandas(df, npartitions=1)
    
        assert_eq(s1.dot(s2), dask_s1.dot(dask_s2))
>       assert_eq(s2.dot(df), dask_s2.dot(dask_df))

dask/dataframe/tests/test_dataframe.py:4966: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_0'
value = Index([], dtype='object')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_transform_getitem_works[max] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'max'

    @pytest.mark.parametrize("func", ["max", "sum"])
    def test_transform_getitem_works(func):
        df = pd.DataFrame({"ints": [1, 2, 3], "grouper": [0, 1, 0]})
    
        ddf = dd.from_pandas(df, npartitions=2)
    
        # what happens here is not exactly a transform, but a reduction
        # the result of which is broadcasted back to the original shape.
        # Broadcasting an aggregation in this manner is very performant in pandas
        meta = df.groupby("grouper").transform(func)
>       df["new"] = df.groupby("grouper").transform(func)["ints"]

dask/dataframe/tests/test_dataframe.py:5161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    ints  grouper
0     1        0
1     2        1
2     3        0
key = 'new', value = 0    3
1    2
2    3
Name: ints, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_transform_getitem_works[sum] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'sum'

    @pytest.mark.parametrize("func", ["max", "sum"])
    def test_transform_getitem_works(func):
        df = pd.DataFrame({"ints": [1, 2, 3], "grouper": [0, 1, 0]})
    
        ddf = dd.from_pandas(df, npartitions=2)
    
        # what happens here is not exactly a transform, but a reduction
        # the result of which is broadcasted back to the original shape.
        # Broadcasting an aggregation in this manner is very performant in pandas
        meta = df.groupby("grouper").transform(func)
>       df["new"] = df.groupby("grouper").transform(func)["ints"]

dask/dataframe/tests/test_dataframe.py:5161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    ints  grouper
0     1        0
1     2        1
2     3        0
key = 'new', value = 0    4
1    2
2    4
Name: ints, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_groupby_on_index[disk-sync] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

scheduler = 'sync'

    @pytest.mark.parametrize("scheduler", ["sync", "threads"])
    def test_groupby_on_index(scheduler):
        pdf = pd.DataFrame(
            {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]},
            index=[0, 1, 3, 5, 6, 8, 9, 9, 9],
        )
        ddf = dd.from_pandas(pdf, npartitions=3)
    
        ddf2 = ddf.set_index("a")
        pdf2 = pdf.set_index("a")
        assert_eq(ddf.groupby("a").b.mean(), ddf2.groupby(ddf2.index).b.mean())
    
        # Check column projection for `groupby().agg`
        agg = ddf2.groupby("a").agg({"b": "mean"})
        assert_eq(ddf.groupby("a").b.mean(), agg.b)
    
        def func(df):
            return df.assign(b=df.b - df.b.mean())
    
        def func2(df):
            return df[["b"]] - df[["b"]].mean()
    
        def func3(df):
            return df.mean()
    
        with dask.config.set(scheduler=scheduler):
            with warnings.catch_warnings():
                warnings.simplefilter("ignore", UserWarning)
                assert_eq(
                    ddf.groupby("a").apply(func, **INCLUDE_GROUPS),
                    pdf.groupby("a").apply(func, **INCLUDE_GROUPS),
                )
    
>               assert_eq(
                    pdf2.groupby(pdf2.index).apply(func2, **INCLUDE_GROUPS),
                    ddf2.groupby(ddf2.index).apply(func2, **INCLUDE_GROUPS),
                )

dask/dataframe/tests/test_groupby.py:252: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [b]
Index: [], key = '_by_0'
value = Index([], dtype='int64', name='a')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_groupby_on_index[disk-threads] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

scheduler = 'threads'

    @pytest.mark.parametrize("scheduler", ["sync", "threads"])
    def test_groupby_on_index(scheduler):
        pdf = pd.DataFrame(
            {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]},
            index=[0, 1, 3, 5, 6, 8, 9, 9, 9],
        )
        ddf = dd.from_pandas(pdf, npartitions=3)
    
        ddf2 = ddf.set_index("a")
        pdf2 = pdf.set_index("a")
        assert_eq(ddf.groupby("a").b.mean(), ddf2.groupby(ddf2.index).b.mean())
    
        # Check column projection for `groupby().agg`
        agg = ddf2.groupby("a").agg({"b": "mean"})
        assert_eq(ddf.groupby("a").b.mean(), agg.b)
    
        def func(df):
            return df.assign(b=df.b - df.b.mean())
    
        def func2(df):
            return df[["b"]] - df[["b"]].mean()
    
        def func3(df):
            return df.mean()
    
        with dask.config.set(scheduler=scheduler):
            with warnings.catch_warnings():
                warnings.simplefilter("ignore", UserWarning)
                assert_eq(
                    ddf.groupby("a").apply(func, **INCLUDE_GROUPS),
                    pdf.groupby("a").apply(func, **INCLUDE_GROUPS),
                )
    
>               assert_eq(
                    pdf2.groupby(pdf2.index).apply(func2, **INCLUDE_GROUPS),
                    ddf2.groupby(ddf2.index).apply(func2, **INCLUDE_GROUPS),
                )

dask/dataframe/tests/test_groupby.py:252: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [b]
Index: [], key = '_by_0'
value = Index([], dtype='int64', name='a')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_groupby_on_index[tasks-sync] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

scheduler = 'sync'

    @pytest.mark.parametrize("scheduler", ["sync", "threads"])
    def test_groupby_on_index(scheduler):
        pdf = pd.DataFrame(
            {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]},
            index=[0, 1, 3, 5, 6, 8, 9, 9, 9],
        )
        ddf = dd.from_pandas(pdf, npartitions=3)
    
        ddf2 = ddf.set_index("a")
        pdf2 = pdf.set_index("a")
        assert_eq(ddf.groupby("a").b.mean(), ddf2.groupby(ddf2.index).b.mean())
    
        # Check column projection for `groupby().agg`
        agg = ddf2.groupby("a").agg({"b": "mean"})
        assert_eq(ddf.groupby("a").b.mean(), agg.b)
    
        def func(df):
            return df.assign(b=df.b - df.b.mean())
    
        def func2(df):
            return df[["b"]] - df[["b"]].mean()
    
        def func3(df):
            return df.mean()
    
        with dask.config.set(scheduler=scheduler):
            with warnings.catch_warnings():
                warnings.simplefilter("ignore", UserWarning)
                assert_eq(
                    ddf.groupby("a").apply(func, **INCLUDE_GROUPS),
                    pdf.groupby("a").apply(func, **INCLUDE_GROUPS),
                )
    
>               assert_eq(
                    pdf2.groupby(pdf2.index).apply(func2, **INCLUDE_GROUPS),
                    ddf2.groupby(ddf2.index).apply(func2, **INCLUDE_GROUPS),
                )

dask/dataframe/tests/test_groupby.py:252: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [b]
Index: [], key = '_by_0'
value = Index([], dtype='int64', name='a')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_groupby_on_index[tasks-threads] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

scheduler = 'threads'

    @pytest.mark.parametrize("scheduler", ["sync", "threads"])
    def test_groupby_on_index(scheduler):
        pdf = pd.DataFrame(
            {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]},
            index=[0, 1, 3, 5, 6, 8, 9, 9, 9],
        )
        ddf = dd.from_pandas(pdf, npartitions=3)
    
        ddf2 = ddf.set_index("a")
        pdf2 = pdf.set_index("a")
        assert_eq(ddf.groupby("a").b.mean(), ddf2.groupby(ddf2.index).b.mean())
    
        # Check column projection for `groupby().agg`
        agg = ddf2.groupby("a").agg({"b": "mean"})
        assert_eq(ddf.groupby("a").b.mean(), agg.b)
    
        def func(df):
            return df.assign(b=df.b - df.b.mean())
    
        def func2(df):
            return df[["b"]] - df[["b"]].mean()
    
        def func3(df):
            return df.mean()
    
        with dask.config.set(scheduler=scheduler):
            with warnings.catch_warnings():
                warnings.simplefilter("ignore", UserWarning)
                assert_eq(
                    ddf.groupby("a").apply(func, **INCLUDE_GROUPS),
                    pdf.groupby("a").apply(func, **INCLUDE_GROUPS),
                )
    
>               assert_eq(
                    pdf2.groupby(pdf2.index).apply(func2, **INCLUDE_GROUPS),
                    ddf2.groupby(ddf2.index).apply(func2, **INCLUDE_GROUPS),
                )

dask/dataframe/tests/test_groupby.py:252: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [b]
Index: [], key = '_by_0'
value = Index([], dtype='int64', name='a')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[disk-cumprod-key1-a] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[disk-cumprod-key1-c] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[disk-cumprod-key1-d] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_cumulative[disk-cumprod-key1-sel3] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_cumulative[disk-cumprod-key1-sel4] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[disk-cumcount-a-a] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[disk-cumcount-a-c] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[disk-cumcount-a-d] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[disk-cumcount-a-sel3] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_groupby_apply_tasks[tasks] ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks'

    def test_groupby_apply_tasks(shuffle_method):
        if shuffle_method == "disk":
            pytest.skip("Tasks-only shuffle test")
    
        df = _compat.makeTimeDataFrame()
>       df["A"] = df.A // 0.1

dask/dataframe/tests/test_groupby.py:852: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =                    A         B         C         D
2000-01-03 -1.023306 -0.443151 -1.598037 -1.824640
2000-01-04  2.34....142141  1.633707
2000-02-10 -0.866261 -0.647870  0.500574 -0.425281
2000-02-11  0.396722  0.596739  0.380478 -0.563965
key = 'A'
value = 2000-01-03   -11.0
2000-01-04    23.0
2000-01-05    -3.0
2000-01-06     0.0
2000-01-07   -16.0
2000-01-10    12.0
2000...07   -16.0
2000-02-08    -3.0
2000-02-09   -27.0
2000-02-10    -9.0
2000-02-11     3.0
Freq: B, Name: A, dtype: float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[disk-cumcount-a-sel4] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[disk-cumcount-key1-a] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[disk-cumcount-key1-c] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[disk-cumcount-key1-d] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_cumulative[disk-cumcount-key1-sel3] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_cumulative[disk-cumcount-key1-sel4] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[tasks-cumsum-a-a] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[tasks-cumsum-a-c] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[tasks-cumsum-a-d] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[tasks-cumsum-a-sel3] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[tasks-cumsum-a-sel4] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[tasks-cumsum-key1-a] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[tasks-cumsum-key1-c] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[tasks-cumsum-key1-d] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_cumulative[tasks-cumsum-key1-sel3] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_cumulative[tasks-cumsum-key1-sel4] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[tasks-cumprod-a-a] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[tasks-cumprod-a-c] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[tasks-cumprod-a-d] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[tasks-cumprod-a-sel3] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[tasks-cumprod-a-sel4] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[tasks-cumprod-key1-a] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[tasks-cumprod-key1-c] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[tasks-cumprod-key1-d] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_cumulative[tasks-cumprod-key1-sel3] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_cumulative[tasks-cumprod-key1-sel4] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = ['a', 'b'], sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[tasks-cumcount-a-a] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[tasks-cumcount-a-c] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[tasks-cumcount-a-d] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[tasks-cumcount-a-sel3] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[tasks-cumcount-a-sel4] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = 'a', sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[tasks-cumcount-key1-a] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[tasks-cumcount-key1-c] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[tasks-cumcount-key1-d] ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_cumulative[tasks-cumcount-key1-sel3] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_cumulative[tasks-cumcount-key1-sel4] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumcount', key = ['a', 'b'], sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_series_groupby_multi_character_column_name[disk] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_series_groupby_multi_character_column_name():
        df = pd.DataFrame({"aa": [1, 2, 1, 3, 4, 1, 2]})
        ddf = dd.from_pandas(df, npartitions=3)
>       assert_eq(df.groupby("aa").aa.cumsum(), ddf.groupby("aa").aa.cumsum())

dask/dataframe/tests/test_groupby.py:1387: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [aa]
Index: [], key = '_by_aa'
value = Series([], Name: aa, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_series_groupby_multi_character_column_name[tasks] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_series_groupby_multi_character_column_name():
        df = pd.DataFrame({"aa": [1, 2, 1, 3, 4, 1, 2]})
        ddf = dd.from_pandas(df, npartitions=3)
>       assert_eq(df.groupby("aa").aa.cumsum(), ddf.groupby("aa").aa.cumsum())

dask/dataframe/tests/test_groupby.py:1387: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [aa]
Index: [], key = '_by_aa'
value = Series([], Name: aa, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_groupby_unaligned_index[disk] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_groupby_unaligned_index():
        df = pd.DataFrame(
            {
                "a": np.random.randint(0, 10, 50),
                "b": np.random.randn(50),
                "c": np.random.randn(50),
            }
        )
        ddf = dd.from_pandas(df, npartitions=5)
        filtered = df[df.b < 0.5]
        dfiltered = ddf[ddf.b < 0.5]
    
        ddf_group = dfiltered.groupby(ddf.a)
        ds_group = dfiltered.b.groupby(ddf.a)
    
        bad = [
            ddf_group.mean(),
            ddf_group.var(),
            ddf_group.b.nunique(),
            ddf_group.get_group(0),
            ds_group.mean(),
            ds_group.var(),
            ds_group.nunique(),
            ds_group.get_group(0),
        ]
    
        for obj in bad:
            with pytest.raises(ValueError):
                obj.compute()
    
        def add1(x):
            return x + 1
    
        df_group = filtered.groupby(df.a)
        expected = df_group.apply(add1, **INCLUDE_GROUPS)
>       assert_eq(ddf_group.apply(add1, meta=expected, **INCLUDE_GROUPS), expected)

dask/dataframe/tests/test_groupby.py:1425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b, c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_groupby_unaligned_index[tasks] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_groupby_unaligned_index():
        df = pd.DataFrame(
            {
                "a": np.random.randint(0, 10, 50),
                "b": np.random.randn(50),
                "c": np.random.randn(50),
            }
        )
        ddf = dd.from_pandas(df, npartitions=5)
        filtered = df[df.b < 0.5]
        dfiltered = ddf[ddf.b < 0.5]
    
        ddf_group = dfiltered.groupby(ddf.a)
        ds_group = dfiltered.b.groupby(ddf.a)
    
        bad = [
            ddf_group.mean(),
            ddf_group.var(),
            ddf_group.b.nunique(),
            ddf_group.get_group(0),
            ds_group.mean(),
            ds_group.var(),
            ds_group.nunique(),
            ds_group.get_group(0),
        ]
    
        for obj in bad:
            with pytest.raises(ValueError):
                obj.compute()
    
        def add1(x):
            return x + 1
    
        df_group = filtered.groupby(df.a)
        expected = df_group.apply(add1, **INCLUDE_GROUPS)
>       assert_eq(ddf_group.apply(add1, meta=expected, **INCLUDE_GROUPS), expected)

dask/dataframe/tests/test_groupby.py:1425: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b, c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_groupby_dataframe_cum_caching[disk-cumsum] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

op = 'cumsum'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("op", ["cumsum", "cumprod"])
    def test_groupby_dataframe_cum_caching(op):
        """Test caching behavior of cumulative operations on grouped dataframes.
    
        Relates to #3756.
        """
        df = pd.DataFrame(
            dict(a=list("aabbcc")), index=pd.date_range(start="20100101", periods=6)
        )
        df["ones"] = 1
        df["twos"] = 2
    
        ddf = dd.from_pandas(df, npartitions=3)
    
        ddf0 = getattr(ddf.groupby(["a"]), op)()
        ddf1 = ddf.rename(columns={"ones": "foo", "twos": "bar"})
        ddf1 = getattr(ddf1.groupby(["a"]), op)()
    
        # _a and _b dataframe should be equal
>       res0_a, res1_a = dask.compute(ddf0, ddf1)

dask/dataframe/tests/test_groupby.py:1467: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [foo, bar]
Index: [], key = '_by_a'
value = Series([], Freq: D, Name: a, dtype: string)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_groupby_dataframe_cum_caching[disk-cumprod] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

op = 'cumprod'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("op", ["cumsum", "cumprod"])
    def test_groupby_dataframe_cum_caching(op):
        """Test caching behavior of cumulative operations on grouped dataframes.
    
        Relates to #3756.
        """
        df = pd.DataFrame(
            dict(a=list("aabbcc")), index=pd.date_range(start="20100101", periods=6)
        )
        df["ones"] = 1
        df["twos"] = 2
    
        ddf = dd.from_pandas(df, npartitions=3)
    
        ddf0 = getattr(ddf.groupby(["a"]), op)()
        ddf1 = ddf.rename(columns={"ones": "foo", "twos": "bar"})
        ddf1 = getattr(ddf1.groupby(["a"]), op)()
    
        # _a and _b dataframe should be equal
>       res0_a, res1_a = dask.compute(ddf0, ddf1)

dask/dataframe/tests/test_groupby.py:1467: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [foo, bar]
Index: [], key = '_by_a'
value = Series([], Freq: D, Name: a, dtype: string)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_groupby_dataframe_cum_caching[tasks-cumsum] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

op = 'cumsum'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("op", ["cumsum", "cumprod"])
    def test_groupby_dataframe_cum_caching(op):
        """Test caching behavior of cumulative operations on grouped dataframes.
    
        Relates to #3756.
        """
        df = pd.DataFrame(
            dict(a=list("aabbcc")), index=pd.date_range(start="20100101", periods=6)
        )
        df["ones"] = 1
        df["twos"] = 2
    
        ddf = dd.from_pandas(df, npartitions=3)
    
        ddf0 = getattr(ddf.groupby(["a"]), op)()
        ddf1 = ddf.rename(columns={"ones": "foo", "twos": "bar"})
        ddf1 = getattr(ddf1.groupby(["a"]), op)()
    
        # _a and _b dataframe should be equal
>       res0_a, res1_a = dask.compute(ddf0, ddf1)

dask/dataframe/tests/test_groupby.py:1467: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [foo, bar]
Index: [], key = '_by_a'
value = Series([], Freq: D, Name: a, dtype: string)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_dataframe_cum_caching[tasks-cumprod] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

op = 'cumprod'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("op", ["cumsum", "cumprod"])
    def test_groupby_dataframe_cum_caching(op):
        """Test caching behavior of cumulative operations on grouped dataframes.
    
        Relates to #3756.
        """
        df = pd.DataFrame(
            dict(a=list("aabbcc")), index=pd.date_range(start="20100101", periods=6)
        )
        df["ones"] = 1
        df["twos"] = 2
    
        ddf = dd.from_pandas(df, npartitions=3)
    
        ddf0 = getattr(ddf.groupby(["a"]), op)()
        ddf1 = ddf.rename(columns={"ones": "foo", "twos": "bar"})
        ddf1 = getattr(ddf1.groupby(["a"]), op)()
    
        # _a and _b dataframe should be equal
>       res0_a, res1_a = dask.compute(ddf0, ddf1)

dask/dataframe/tests/test_groupby.py:1467: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [foo, bar]
Index: [], key = '_by_a'
value = Series([], Freq: D, Name: a, dtype: string)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_groupby_column_and_index_agg_funcs[disk-cumprod] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

agg_func = 'cumprod'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize(
        "agg_func",
        [
            "cumprod",
            "cumcount",
            "cumsum",
            "var",
            "sum",
            "mean",
            "count",
            "size",
            "std",
            "min",
            "max",
            "first",
            "last",
            "prod",
        ],
    )
    def test_groupby_column_and_index_agg_funcs(agg_func):
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        df = pd.DataFrame(
            {
                "idx": [1, 1, 1, 2, 2, 2],
                "a": [1, 2, 1, 2, 1, 2],
                "b": np.arange(6),
                "c": [1, 1, 1, 2, 2, 2],
            }
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(df, npartitions=df.index.nunique(), sort=False)
    
        # Index and then column
    
        # Compute expected result
        expected = call(df.groupby(["idx", "a"]), agg_func)
        if agg_func in {"mean", "var"}:
            expected = expected.astype(float)
    
        result = call(ddf.groupby(["idx", "a"]), agg_func)
>       assert_eq(expected, result)

dask/dataframe/tests/test_groupby.py:1550: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [b, c]
Index: [], key = '_by_idx'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_groupby_column_and_index_agg_funcs[disk-cumcount] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

agg_func = 'cumcount'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize(
        "agg_func",
        [
            "cumprod",
            "cumcount",
            "cumsum",
            "var",
            "sum",
            "mean",
            "count",
            "size",
            "std",
            "min",
            "max",
            "first",
            "last",
            "prod",
        ],
    )
    def test_groupby_column_and_index_agg_funcs(agg_func):
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        df = pd.DataFrame(
            {
                "idx": [1, 1, 1, 2, 2, 2],
                "a": [1, 2, 1, 2, 1, 2],
                "b": np.arange(6),
                "c": [1, 1, 1, 2, 2, 2],
            }
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(df, npartitions=df.index.nunique(), sort=False)
    
        # Index and then column
    
        # Compute expected result
        expected = call(df.groupby(["idx", "a"]), agg_func)
        if agg_func in {"mean", "var"}:
            expected = expected.astype(float)
    
        result = call(ddf.groupby(["idx", "a"]), agg_func)
>       assert_eq(expected, result)

dask/dataframe/tests/test_groupby.py:1550: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_idx'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_groupby_column_and_index_agg_funcs[disk-cumsum] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

agg_func = 'cumsum'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize(
        "agg_func",
        [
            "cumprod",
            "cumcount",
            "cumsum",
            "var",
            "sum",
            "mean",
            "count",
            "size",
            "std",
            "min",
            "max",
            "first",
            "last",
            "prod",
        ],
    )
    def test_groupby_column_and_index_agg_funcs(agg_func):
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        df = pd.DataFrame(
            {
                "idx": [1, 1, 1, 2, 2, 2],
                "a": [1, 2, 1, 2, 1, 2],
                "b": np.arange(6),
                "c": [1, 1, 1, 2, 2, 2],
            }
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(df, npartitions=df.index.nunique(), sort=False)
    
        # Index and then column
    
        # Compute expected result
        expected = call(df.groupby(["idx", "a"]), agg_func)
        if agg_func in {"mean", "var"}:
            expected = expected.astype(float)
    
        result = call(ddf.groupby(["idx", "a"]), agg_func)
>       assert_eq(expected, result)

dask/dataframe/tests/test_groupby.py:1550: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [b, c]
Index: [], key = '_by_idx'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_groupby_column_and_index_agg_funcs[tasks-cumprod] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

agg_func = 'cumprod'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize(
        "agg_func",
        [
            "cumprod",
            "cumcount",
            "cumsum",
            "var",
            "sum",
            "mean",
            "count",
            "size",
            "std",
            "min",
            "max",
            "first",
            "last",
            "prod",
        ],
    )
    def test_groupby_column_and_index_agg_funcs(agg_func):
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        df = pd.DataFrame(
            {
                "idx": [1, 1, 1, 2, 2, 2],
                "a": [1, 2, 1, 2, 1, 2],
                "b": np.arange(6),
                "c": [1, 1, 1, 2, 2, 2],
            }
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(df, npartitions=df.index.nunique(), sort=False)
    
        # Index and then column
    
        # Compute expected result
        expected = call(df.groupby(["idx", "a"]), agg_func)
        if agg_func in {"mean", "var"}:
            expected = expected.astype(float)
    
        result = call(ddf.groupby(["idx", "a"]), agg_func)
>       assert_eq(expected, result)

dask/dataframe/tests/test_groupby.py:1550: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [b, c]
Index: [], key = '_by_idx'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_groupby_column_and_index_agg_funcs[tasks-cumcount] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

agg_func = 'cumcount'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize(
        "agg_func",
        [
            "cumprod",
            "cumcount",
            "cumsum",
            "var",
            "sum",
            "mean",
            "count",
            "size",
            "std",
            "min",
            "max",
            "first",
            "last",
            "prod",
        ],
    )
    def test_groupby_column_and_index_agg_funcs(agg_func):
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        df = pd.DataFrame(
            {
                "idx": [1, 1, 1, 2, 2, 2],
                "a": [1, 2, 1, 2, 1, 2],
                "b": np.arange(6),
                "c": [1, 1, 1, 2, 2, 2],
            }
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(df, npartitions=df.index.nunique(), sort=False)
    
        # Index and then column
    
        # Compute expected result
        expected = call(df.groupby(["idx", "a"]), agg_func)
        if agg_func in {"mean", "var"}:
            expected = expected.astype(float)
    
        result = call(ddf.groupby(["idx", "a"]), agg_func)
>       assert_eq(expected, result)

dask/dataframe/tests/test_groupby.py:1550: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [0]
Index: [], key = '_by_idx'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_groupby_column_and_index_agg_funcs[tasks-cumsum] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

agg_func = 'cumsum'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize(
        "agg_func",
        [
            "cumprod",
            "cumcount",
            "cumsum",
            "var",
            "sum",
            "mean",
            "count",
            "size",
            "std",
            "min",
            "max",
            "first",
            "last",
            "prod",
        ],
    )
    def test_groupby_column_and_index_agg_funcs(agg_func):
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        df = pd.DataFrame(
            {
                "idx": [1, 1, 1, 2, 2, 2],
                "a": [1, 2, 1, 2, 1, 2],
                "b": np.arange(6),
                "c": [1, 1, 1, 2, 2, 2],
            }
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(df, npartitions=df.index.nunique(), sort=False)
    
        # Index and then column
    
        # Compute expected result
        expected = call(df.groupby(["idx", "a"]), agg_func)
        if agg_func in {"mean", "var"}:
            expected = expected.astype(float)
    
        result = call(ddf.groupby(["idx", "a"]), agg_func)
>       assert_eq(expected, result)

dask/dataframe/tests/test_groupby.py:1550: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [b, c]
Index: [], key = '_by_idx'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_groupby_column_and_index_apply[disk-min-group_args0] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx', 'a'], apply_func = <function min at 0x7f5c15d08530>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_groupby_column_and_index_apply[disk-min-group_args2] ___________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx'], apply_func = <function min at 0x7f5c15d08530>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_column_and_index_apply[disk-min-idx] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = 'idx', apply_func = <function min at 0x7f5c15d08530>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_groupby_column_and_index_apply[disk-mean-group_args0] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx', 'a'], apply_func = <function mean at 0x7f5c15d08fb0>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_groupby_column_and_index_apply[disk-mean-group_args2] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx'], apply_func = <function mean at 0x7f5c15d08fb0>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_column_and_index_apply[disk-mean-idx] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = 'idx', apply_func = <function mean at 0x7f5c15d08fb0>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_groupby_column_and_index_apply[disk-<lambda>-group_args0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx', 'a'], apply_func = <function <lambda> at 0x7f5bf4d46c40>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_groupby_column_and_index_apply[disk-<lambda>-group_args2] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx'], apply_func = <function <lambda> at 0x7f5bf4d46c40>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________ test_groupby_column_and_index_apply[disk-<lambda>-idx] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = 'idx', apply_func = <function <lambda> at 0x7f5bf4d46c40>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_groupby_column_and_index_apply[tasks-min-group_args0] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx', 'a'], apply_func = <function min at 0x7f5c15d08530>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_groupby_column_and_index_apply[tasks-min-group_args2] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx'], apply_func = <function min at 0x7f5c15d08530>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949d20>, agg_func = 'median'
split_out = 1

    @pytest.mark.flaky(reruns=5)  # See https://github.com/dask/dask/issues/9793
    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: ["a"],
            lambda df: ["a", "b"],
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_dataframe_aggregations_multilevel(grouper, agg_func, split_out):
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "d": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a", "d"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
        # covariance only works with N+1 columns
        if agg_func not in ("cov", "corr"):
>           assert_eq(
                call(pdf.groupby(grouper(pdf), sort=sort)["c"], agg_func),
                call(
                    ddf.groupby(grouper(ddf), sort=sort)["c"],
                    agg_func,
                    split_out=split_out,
                    split_every=2,
                ),
            )

dask/dataframe/tests/test_groupby.py:1132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: bool)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_column_and_index_apply[tasks-min-idx] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = 'idx', apply_func = <function min at 0x7f5c15d08530>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_groupby_column_and_index_apply[tasks-mean-group_args0] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx', 'a'], apply_func = <function mean at 0x7f5c15d08fb0>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_groupby_column_and_index_apply[tasks-mean-group_args2] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx'], apply_func = <function mean at 0x7f5c15d08fb0>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_groupby_column_and_index_apply[tasks-mean-idx] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = 'idx', apply_func = <function mean at 0x7f5c15d08fb0>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ test_groupby_column_and_index_apply[tasks-<lambda>-group_args0] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx', 'a'], apply_func = <function <lambda> at 0x7f5bf4d46c40>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ test_groupby_column_and_index_apply[tasks-<lambda>-group_args2] ________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = ['idx'], apply_func = <function <lambda> at 0x7f5bf4d46c40>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_groupby_column_and_index_apply[tasks-<lambda>-idx] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_args = 'idx', apply_func = <function <lambda> at 0x7f5bf4d46c40>

    @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"])
    @pytest.mark.parametrize(
        "apply_func",
        [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)],
    )
    def test_groupby_column_and_index_apply(group_args, apply_func):
        df = pd.DataFrame(
            {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)}
        ).set_index("idx")
    
        ddf = dd.from_pandas(df, npartitions=df.index.nunique())
        ddf_no_divs = dd.from_pandas(
            df, npartitions=df.index.nunique(), sort=False
        ).clear_divisions()
    
        # Expected result
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame with divisions (no shuffling)
        result = ddf.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
        assert_eq(expected, result, check_divisions=False)
    
        # Check that partitioning is preserved
        assert ddf.divisions == result.divisions
    
        # Check that no shuffling occurred.
        # The groupby operation should add only 1 task per partition
        assert len(result.dask) == (len(ddf.dask) + ddf.npartitions)
    
        expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS)
    
        # Compute on dask DataFrame without divisions (requires shuffling)
        result = ddf_no_divs.groupby(group_args).apply(
            apply_func, axis=0, meta=expected, **INCLUDE_GROUPS
        )
    
>       assert_eq(expected, result, check_divisions=False)

dask/dataframe/tests/test_groupby.py:1638: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] ________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949d20>, agg_func = 'median'
split_out = 2

    @pytest.mark.flaky(reruns=5)  # See https://github.com/dask/dask/issues/9793
    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: ["a"],
            lambda df: ["a", "b"],
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_dataframe_aggregations_multilevel(grouper, agg_func, split_out):
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "d": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a", "d"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
        # covariance only works with N+1 columns
        if agg_func not in ("cov", "corr"):
>           assert_eq(
                call(pdf.groupby(grouper(pdf), sort=sort)["c"], agg_func),
                call(
                    ddf.groupby(grouper(ddf), sort=sort)["c"],
                    agg_func,
                    split_out=split_out,
                    split_every=2,
                ),
            )

dask/dataframe/tests/test_groupby.py:1132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: bool)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949d20>, agg_func = 'median'
split_out = 1

    @pytest.mark.flaky(reruns=5)  # See https://github.com/dask/dask/issues/9793
    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: ["a"],
            lambda df: ["a", "b"],
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_dataframe_aggregations_multilevel(grouper, agg_func, split_out):
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "d": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a", "d"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
        # covariance only works with N+1 columns
        if agg_func not in ("cov", "corr"):
>           assert_eq(
                call(pdf.groupby(grouper(pdf), sort=sort)["c"], agg_func),
                call(
                    ddf.groupby(grouper(ddf), sort=sort)["c"],
                    agg_func,
                    split_out=split_out,
                    split_every=2,
                ),
            )

dask/dataframe/tests/test_groupby.py:1132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: bool)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_groupby_group_keys[disk-True] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_keys = True

    @pytest.mark.parametrize("group_keys", [True, False, None])
    def test_groupby_group_keys(group_keys):
        df = pd.DataFrame({"a": [1, 2, 2, 3], "b": [2, 3, 4, 5]})
        ddf = dd.from_pandas(df, npartitions=2).set_index("a")
        pdf = df.set_index("a")
    
        func = lambda g: g.copy()
        expected = pdf.groupby("a").apply(func, **INCLUDE_GROUPS)
>       assert_eq(expected, ddf.groupby("a").apply(func, meta=expected, **INCLUDE_GROUPS))

dask/dataframe/tests/test_groupby.py:1893: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_groupby_group_keys[disk-False] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_keys = False

    @pytest.mark.parametrize("group_keys", [True, False, None])
    def test_groupby_group_keys(group_keys):
        df = pd.DataFrame({"a": [1, 2, 2, 3], "b": [2, 3, 4, 5]})
        ddf = dd.from_pandas(df, npartitions=2).set_index("a")
        pdf = df.set_index("a")
    
        func = lambda g: g.copy()
        expected = pdf.groupby("a").apply(func, **INCLUDE_GROUPS)
>       assert_eq(expected, ddf.groupby("a").apply(func, meta=expected, **INCLUDE_GROUPS))

dask/dataframe/tests/test_groupby.py:1893: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_groupby_group_keys[disk-None] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_keys = None

    @pytest.mark.parametrize("group_keys", [True, False, None])
    def test_groupby_group_keys(group_keys):
        df = pd.DataFrame({"a": [1, 2, 2, 3], "b": [2, 3, 4, 5]})
        ddf = dd.from_pandas(df, npartitions=2).set_index("a")
        pdf = df.set_index("a")
    
        func = lambda g: g.copy()
        expected = pdf.groupby("a").apply(func, **INCLUDE_GROUPS)
>       assert_eq(expected, ddf.groupby("a").apply(func, meta=expected, **INCLUDE_GROUPS))

dask/dataframe/tests/test_groupby.py:1893: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______ test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] _______
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949d20>, agg_func = 'median'
split_out = 2

    @pytest.mark.flaky(reruns=5)  # See https://github.com/dask/dask/issues/9793
    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: ["a"],
            lambda df: ["a", "b"],
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_dataframe_aggregations_multilevel(grouper, agg_func, split_out):
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "d": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a", "d"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
        # covariance only works with N+1 columns
        if agg_func not in ("cov", "corr"):
>           assert_eq(
                call(pdf.groupby(grouper(pdf), sort=sort)["c"], agg_func),
                call(
                    ddf.groupby(grouper(ddf), sort=sort)["c"],
                    agg_func,
                    split_out=split_out,
                    split_every=2,
                ),
            )

dask/dataframe/tests/test_groupby.py:1132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: bool)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_groupby_group_keys[tasks-True] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_keys = True

    @pytest.mark.parametrize("group_keys", [True, False, None])
    def test_groupby_group_keys(group_keys):
        df = pd.DataFrame({"a": [1, 2, 2, 3], "b": [2, 3, 4, 5]})
        ddf = dd.from_pandas(df, npartitions=2).set_index("a")
        pdf = df.set_index("a")
    
        func = lambda g: g.copy()
        expected = pdf.groupby("a").apply(func, **INCLUDE_GROUPS)
>       assert_eq(expected, ddf.groupby("a").apply(func, meta=expected, **INCLUDE_GROUPS))

dask/dataframe/tests/test_groupby.py:1893: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_groupby_group_keys[tasks-False] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_keys = False

    @pytest.mark.parametrize("group_keys", [True, False, None])
    def test_groupby_group_keys(group_keys):
        df = pd.DataFrame({"a": [1, 2, 2, 3], "b": [2, 3, 4, 5]})
        ddf = dd.from_pandas(df, npartitions=2).set_index("a")
        pdf = df.set_index("a")
    
        func = lambda g: g.copy()
        expected = pdf.groupby("a").apply(func, **INCLUDE_GROUPS)
>       assert_eq(expected, ddf.groupby("a").apply(func, meta=expected, **INCLUDE_GROUPS))

dask/dataframe/tests/test_groupby.py:1893: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_groupby_group_keys[tasks-None] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

group_keys = None

    @pytest.mark.parametrize("group_keys", [True, False, None])
    def test_groupby_group_keys(group_keys):
        df = pd.DataFrame({"a": [1, 2, 2, 3], "b": [2, 3, 4, 5]})
        ddf = dd.from_pandas(df, npartitions=2).set_index("a")
        pdf = df.set_index("a")
    
        func = lambda g: g.copy()
        expected = pdf.groupby("a").apply(func, **INCLUDE_GROUPS)
>       assert_eq(expected, ddf.groupby("a").apply(func, meta=expected, **INCLUDE_GROUPS))

dask/dataframe/tests/test_groupby.py:1893: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_groupby_shift_with_freq[disk] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'disk'

    def test_groupby_shift_with_freq(shuffle_method):
        pdf = pd.DataFrame(
            dict(a=[1, 2, 3, 4, 5, 6], b=[0, 0, 0, 1, 1, 1]),
            index=pd.date_range(start="20100101", periods=6),
        )
        ddf = dd.from_pandas(pdf, npartitions=3)
    
        # just pass the pandas result as meta for convenience
        df_result = pdf.groupby(pdf.index).shift(periods=-2, freq="D")
        # Groupby/shift on the index should avoid shuffle and let the `freq` pass
        # unmodified, but that is currently broken: https://github.com/dask/dask/issues/8959
        # TODO: remove check_freq condition once fixed.
>       assert_eq(
            df_result,
            ddf.groupby(ddf.index).shift(periods=-2, freq="D", meta=df_result),
            check_freq=False,
        )

dask/dataframe/tests/test_groupby.py:2240: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_0'
value = DatetimeIndex([], dtype='datetime64[ns]', freq='D')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_groupby_shift_with_freq[tasks] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks'

    def test_groupby_shift_with_freq(shuffle_method):
        pdf = pd.DataFrame(
            dict(a=[1, 2, 3, 4, 5, 6], b=[0, 0, 0, 1, 1, 1]),
            index=pd.date_range(start="20100101", periods=6),
        )
        ddf = dd.from_pandas(pdf, npartitions=3)
    
        # just pass the pandas result as meta for convenience
        df_result = pdf.groupby(pdf.index).shift(periods=-2, freq="D")
        # Groupby/shift on the index should avoid shuffle and let the `freq` pass
        # unmodified, but that is currently broken: https://github.com/dask/dask/issues/8959
        # TODO: remove check_freq condition once fixed.
>       assert_eq(
            df_result,
            ddf.groupby(ddf.index).shift(periods=-2, freq="D", meta=df_result),
            check_freq=False,
        )

dask/dataframe/tests/test_groupby.py:2240: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_0'
value = DatetimeIndex([], dtype='datetime64[ns]', freq='D')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[disk-column0-2] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 2, column = ['b', 'c']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[disk-column0-3] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 3, column = ['b', 'c']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[disk-column1-2] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 2, column = ['b', 'd']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[disk-column1-3] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 3, column = ['b', 'd']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[disk-column2-2] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 2, column = ['b', 'e']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[disk-column2-3] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 3, column = ['b', 'e']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[tasks-column0-2] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 2, column = ['b', 'c']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[tasks-column0-3] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 3, column = ['b', 'c']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[tasks-column1-2] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 2, column = ['b', 'd']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[tasks-column1-3] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 3, column = ['b', 'd']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[tasks-column2-2] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 2, column = ['b', 'e']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_groupby_split_out_multiindex[tasks-column2-3] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

split_out = 3, column = ['b', 'e']

    @pytest.mark.parametrize("split_out", [2, 3])
    @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]])
    def test_groupby_split_out_multiindex(split_out, column):
        df = pd.DataFrame(
            {
                "a": np.arange(8),
                "b": [1, 0, 0, 2, 1, 1, 2, 0],
                "c": [0, 1] * 4,
                "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"],
            }
        ).fillna(0)
>       df["e"] = df["d"].astype("category")

dask/dataframe/tests/test_groupby.py:2628: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c     d
0  0  1  0   dog
1  1  0  1   cat
2  2  0  0   cat
3  3  2  1   dog
4  4  1  0   dog
5  5  1  1   dog
6  6  2  0   cat
7  7  0  1  bird
key = 'e'
value = 0     dog
1     cat
2     cat
3     dog
4     dog
5     dog
6     cat
7    bird
Name: d, dtype: category
Categories (3, object): ['bird', 'cat', 'dog']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_known[on2-right-disk] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'right', shuffle_method = 'disk'

    def test_merge_known_to_known(
        df_left, df_right, ddf_left, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_known[on2-right-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'right', shuffle_method = 'tasks'

    def test_merge_known_to_known(
        df_left, df_right, ddf_left, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_known[on2-outer-disk] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'outer', shuffle_method = 'disk'

    def test_merge_known_to_known(
        df_left, df_right, ddf_left, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0   0.0
1     0  1.0   1.0   1.0
2     0  2.0   2.0   2.0
3     0  NaN   NaN...  10  NaN   NaN  49.0
63   10  NaN   NaN  50.0
64   10  NaN   NaN  51.0
65   10  NaN   NaN  52.0

[66 rows x 4 columns]
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
     ..
61    3
62    4
63    5
64    6
65    7
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_known[on2-outer-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'outer', shuffle_method = 'tasks'

    def test_merge_known_to_known(
        df_left, df_right, ddf_left, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0   0.0
1     0  1.0   1.0   1.0
2     0  2.0   2.0   2.0
3     0  NaN   NaN...  10  NaN   NaN  49.0
63   10  NaN   NaN  50.0
64   10  NaN   NaN  51.0
65   10  NaN   NaN  52.0

[66 rows x 4 columns]
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
     ..
61    3
62    4
63    5
64    6
65    7
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_known[on3-right-disk] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'right', shuffle_method = 'disk'

    def test_merge_known_to_known(
        df_left, df_right, ddf_left, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_known[on3-right-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'right', shuffle_method = 'tasks'

    def test_merge_known_to_known(
        df_left, df_right, ddf_left, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_known[on3-outer-disk] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'outer', shuffle_method = 'disk'

    def test_merge_known_to_known(
        df_left, df_right, ddf_left, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0   0.0
1   0.0   3.0   4.0
2   0.0   7.0   6.0
3   0.0   9.0  11.0
4   0.0  14.0  14.... NaN   NaN  28.0
62  7.0  31.0   NaN
63  NaN   NaN  52.0
64  NaN   NaN  29.0
65  8.0  32.0   NaN

[66 rows x 3 columns]
key = 'k'
value = 0     0
1     0
2     0
3     0
4     0
     ..
61    7
62    7
63    7
64    8
65    8
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_known[on3-outer-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'outer', shuffle_method = 'tasks'

    def test_merge_known_to_known(
        df_left, df_right, ddf_left, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0   0.0
1   0.0   3.0   4.0
2   0.0   7.0   6.0
3   0.0   9.0  11.0
4   0.0  14.0  14.... NaN   NaN  28.0
62  7.0  31.0   NaN
63  NaN   NaN  52.0
64  NaN   NaN  29.0
65  8.0  32.0   NaN

[66 rows x 3 columns]
key = 'k'
value = 0     0
1     0
2     0
3     0
4     0
     ..
61    7
62    7
63    7
64    8
65    8
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_single_to_known[on2-disk-right] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_single = Dask DataFrame Structure:
                   k     v1
npartitions=1              
0              int64  int64
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'right', shuffle_method = 'disk'

    @pytest.mark.parametrize("how", ["inner", "right"])
    def test_merge_single_to_known(
        df_left, df_right, ddf_left_single, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_single_to_known[on2-tasks-right] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_single = Dask DataFrame Structure:
                   k     v1
npartitions=1              
0              int64  int64
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'right', shuffle_method = 'tasks'

    @pytest.mark.parametrize("how", ["inner", "right"])
    def test_merge_single_to_known(
        df_left, df_right, ddf_left_single, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_single_to_known[on3-disk-right] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_single = Dask DataFrame Structure:
                   k     v1
npartitions=1              
0              int64  int64
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'right', shuffle_method = 'disk'

    @pytest.mark.parametrize("how", ["inner", "right"])
    def test_merge_single_to_known(
        df_left, df_right, ddf_left_single, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_single_to_known[on3-tasks-right] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_single = Dask DataFrame Structure:
                   k     v1
npartitions=1              
0              int64  int64
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'right', shuffle_method = 'tasks'

    @pytest.mark.parametrize("how", ["inner", "right"])
    def test_merge_single_to_known(
        df_left, df_right, ddf_left_single, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:132: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[idx-inner-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'inner', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[idx-inner-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'inner', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_unknown[idx-left-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'left', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[idx-left-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'left', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[idx-right-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'right', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[idx-right-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'right', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[idx-outer-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'outer', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[idx-outer-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'outer', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on1-inner-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'inner', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on1-inner-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'inner', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_unknown[on1-left-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'left', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on1-left-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'left', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on1-right-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'right', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on1-right-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'right', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on1-outer-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'outer', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on1-outer-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'outer', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on2-inner-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'inner', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on2-inner-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'inner', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_unknown[on2-left-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'left', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on2-left-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'left', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on2-right-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'right', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:149: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on2-right-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'right', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:149: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on2-outer-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'outer', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:149: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0   0.0
1     0  1.0   1.0   1.0
2     0  2.0   2.0   2.0
3     0  NaN   NaN...  10  NaN   NaN  49.0
63   10  NaN   NaN  50.0
64   10  NaN   NaN  51.0
65   10  NaN   NaN  52.0

[66 rows x 4 columns]
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
     ..
61    3
62    4
63    5
64    6
65    7
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on2-outer-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'outer', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:149: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0   0.0
1     0  1.0   1.0   1.0
2     0  2.0   2.0   2.0
3     0  NaN   NaN...  10  NaN   NaN  49.0
63   10  NaN   NaN  50.0
64   10  NaN   NaN  51.0
65   10  NaN   NaN  52.0

[66 rows x 4 columns]
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
     ..
61    3
62    4
63    5
64    6
65    7
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on3-inner-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'inner', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on3-inner-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'inner', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_known_to_unknown[on3-left-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'left', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on3-left-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'left', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:157: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on3-right-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'right', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:149: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on3-right-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'right', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:149: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on3-outer-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'outer', shuffle_method = 'disk'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:149: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0   0.0
1   0.0   3.0   4.0
2   0.0   7.0   6.0
3   0.0   9.0  11.0
4   0.0  14.0  14.... NaN   NaN  28.0
62  7.0  31.0   NaN
63  NaN   NaN  52.0
64  NaN   NaN  29.0
65  8.0  32.0   NaN

[66 rows x 3 columns]
key = 'k'
value = 0     0
1     0
2     0
3     0
4     0
     ..
61    7
62    7
63    7
64    8
65    8
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_known_to_unknown[on3-outer-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'outer', shuffle_method = 'tasks'

    def test_merge_known_to_unknown(
        df_left, df_right, ddf_left, ddf_right_unknown, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:149: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0   0.0
1   0.0   3.0   4.0
2   0.0   7.0   6.0
3   0.0   9.0  11.0
4   0.0  14.0  14.... NaN   NaN  28.0
62  7.0  31.0   NaN
63  NaN   NaN  52.0
64  NaN   NaN  29.0
65  8.0  32.0   NaN

[66 rows x 3 columns]
key = 'k'
value = 0     0
1     0
2     0
3     0
4     0
     ..
61    7
62    7
63    7
64    8
65    8
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[idx-inner-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = 'idx', how = 'inner', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[idx-inner-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = 'idx', how = 'inner', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_aggregations_multilevel[median-disk-1-<lambda>0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949e80>, split_out = 1
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_unknown_to_known[idx-left-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = 'idx', how = 'left', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_aggregations_multilevel[median-disk-1-<lambda>1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949f30>, split_out = 1
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[idx-left-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = 'idx', how = 'left', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_aggregations_multilevel[median-disk-1-<lambda>2] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949fe0>, split_out = 1
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: bool)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[idx-right-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = 'idx', how = 'right', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[idx-right-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = 'idx', how = 'right', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_aggregations_multilevel[median-disk-2-<lambda>0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949e80>, split_out = 2
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[idx-outer-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = 'idx', how = 'outer', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_aggregations_multilevel[median-disk-2-<lambda>1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949f30>, split_out = 2
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[idx-outer-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = 'idx', how = 'outer', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_series_aggregations_multilevel[median-disk-2-<lambda>2] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949fe0>, split_out = 2
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: bool)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on1-inner-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx'], how = 'inner', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on1-inner-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx'], how = 'inner', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_aggregations_multilevel[median-tasks-1-<lambda>0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949e80>, split_out = 1
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_unknown_to_known[on1-left-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx'], how = 'left', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_aggregations_multilevel[median-tasks-1-<lambda>1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949f30>, split_out = 1
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on1-left-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx'], how = 'left', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on1-right-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx'], how = 'right', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_aggregations_multilevel[median-tasks-1-<lambda>2] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949fe0>, split_out = 1
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: bool)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on1-right-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx'], how = 'right', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_aggregations_multilevel[median-tasks-2-<lambda>0] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949e80>, split_out = 2
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on1-outer-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx'], how = 'outer', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_aggregations_multilevel[median-tasks-2-<lambda>1] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949f30>, split_out = 2
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on1-outer-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx'], how = 'outer', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on2-inner-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'inner', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________ test_series_aggregations_multilevel[median-tasks-2-<lambda>2] _________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

grouper = <function <lambda> at 0x7ffb8f949fe0>, split_out = 2
agg_func = 'median'

    @pytest.mark.parametrize(
        "grouper",
        [
            lambda df: df["a"],
            lambda df: [df["a"], df["b"]],
            lambda df: [df["a"] > 2, df["b"] > 1],
        ],
    )
    @pytest.mark.parametrize("split_out", [1, 2])
    def test_series_aggregations_multilevel(grouper, split_out, agg_func):
        """
        similar to ``test_dataframe_aggregations_multilevel``, but series do not
        support all groupby args.
        """
        sort = split_out == 1  # Don't sort for split_out > 1
    
        def call(g, m, **kwargs):
            return getattr(g, m)(**kwargs)
    
        # covariance/correlation is not a series aggregation
        if agg_func in ("cov", "corr"):
            return
    
        pdf = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10,
                "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10,
            },
            columns=["c", "b", "a"],
        )
    
        ddf = dd.from_pandas(pdf, npartitions=10)
    
>       assert_eq(
            call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func),
            call(
                ddf["c"].groupby(grouper(ddf), sort=sort),
                agg_func,
                split_out=split_out,
                split_every=2,
            ),
            # for pandas ~ 0.18, the name is not not properly propagated for
            # the mean aggregation
            check_names=(agg_func not in {"mean", "nunique"}),
        )

dask/dataframe/tests/test_groupby.py:1217: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:598: in assert_divisions
    if not hasattr(ddf, "divisions"):
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:347: in divisions
    return self.expr.divisions
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:431: in divisions
    return tuple(self._divisions())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:57: in _divisions
    x = self.optimize(fuse=False)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:964: in _lower
    map_columns, unmap_columns = get_map_columns(df)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_groupby.py:946: in get_map_columns
    map_columns = {col: str(col) for col in df.columns if col != str(col)}
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_0'
value = Series([], Name: a, dtype: bool)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on2-inner-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'inner', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_unknown_to_known[on2-left-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'left', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on2-left-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'left', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on2-right-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'right', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:165: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on2-right-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'right', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:165: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on2-outer-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'outer', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:165: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0   0.0
1     0  1.0   1.0   1.0
2     0  2.0   2.0   2.0
3     0  NaN   NaN...  10  NaN   NaN  49.0
63   10  NaN   NaN  50.0
64   10  NaN   NaN  51.0
65   10  NaN   NaN  52.0

[66 rows x 4 columns]
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
     ..
61    3
62    4
63    5
64    6
65    7
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on2-outer-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], how = 'outer', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:165: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0   0.0
1     0  1.0   1.0   1.0
2     0  2.0   2.0   2.0
3     0  NaN   NaN...  10  NaN   NaN  49.0
63   10  NaN   NaN  50.0
64   10  NaN   NaN  51.0
65   10  NaN   NaN  52.0

[66 rows x 4 columns]
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
     ..
61    3
62    4
63    5
64    6
65    7
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on3-inner-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'inner', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on3-inner-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'inner', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_merge_unknown_to_known[on3-left-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'left', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on3-left-tasks] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'left', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Perform merge
        result = ddf_left_unknown.merge(
            ddf_right, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:173: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on3-right-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'right', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:165: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on3-right-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'right', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:165: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on3-outer-disk] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'outer', shuffle_method = 'disk'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:165: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0   0.0
1   0.0   3.0   4.0
2   0.0   7.0   6.0
3   0.0   9.0  11.0
4   0.0  14.0  14.... NaN   NaN  28.0
62  7.0  31.0   NaN
63  NaN   NaN  52.0
64  NaN   NaN  29.0
65  8.0  32.0   NaN

[66 rows x 3 columns]
key = 'k'
value = 0     0
1     0
2     0
3     0
4     0
     ..
61    7
62    7
63    7
64    8
65    8
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_known[on3-outer-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], how = 'outer', shuffle_method = 'tasks'

    def test_merge_unknown_to_known(
        df_left, df_right, ddf_left_unknown, ddf_right, on, how, shuffle_method
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:165: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0   0.0
1   0.0   3.0   4.0
2   0.0   7.0   6.0
3   0.0   9.0  11.0
4   0.0  14.0  14.... NaN   NaN  28.0
62  7.0  31.0   NaN
63  NaN   NaN  52.0
64  NaN   NaN  29.0
65  8.0  32.0   NaN

[66 rows x 3 columns]
key = 'k'
value = 0     0
1     0
2     0
3     0
4     0
     ..
61    7
62    7
63    7
64    8
65    8
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[idx-inner-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'inner', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[idx-inner-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'inner', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_unknown[idx-left-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'left', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[idx-left-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'left', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[idx-right-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'right', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[idx-right-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'right', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[idx-outer-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'outer', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[idx-outer-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = 'idx', how = 'outer', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on1-inner-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'inner', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on1-inner-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'inner', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_unknown[on1-left-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'left', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on1-left-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'left', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on1-right-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'right', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on1-right-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'right', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on1-outer-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'outer', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on1-outer-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx'], how = 'outer', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on2-inner-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'inner', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on2-inner-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'inner', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_unknown[on2-left-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'left', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on2-left-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'left', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on2-right-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'right', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on2-right-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'right', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on2-outer-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'outer', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0   0.0
1     0  1.0   1.0   1.0
2     0  2.0   2.0   2.0
3     0  NaN   NaN...  10  NaN   NaN  49.0
63   10  NaN   NaN  50.0
64   10  NaN   NaN  51.0
65   10  NaN   NaN  52.0

[66 rows x 4 columns]
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
     ..
61    3
62    4
63    5
64    6
65    7
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on2-outer-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['idx', 'k'], how = 'outer', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0   0.0
1     0  1.0   1.0   1.0
2     0  2.0   2.0   2.0
3     0  NaN   NaN...  10  NaN   NaN  49.0
63   10  NaN   NaN  50.0
64   10  NaN   NaN  51.0
65   10  NaN   NaN  52.0

[66 rows x 4 columns]
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
     ..
61    3
62    4
63    5
64    6
65    7
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on3-inner-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'inner', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on3-inner-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'inner', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_unknown_to_unknown[on3-left-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'left', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on3-left-tasks] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'left', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
        expected = df_left.merge(df_right, on=on, how=how)
    
        # Merge unknown to unknown
        result = ddf_left_unknown.merge(
            ddf_right_unknown, on=on, how=how, shuffle_method=shuffle_method
        )
    
        # Assertions
>       assert_eq(result, expected)

dask/dataframe/tests/test_merge_column_and_index.py:195: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:272: in _lower
    frame._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [k, v1]
Index: [], key = '_partitions_0'
value = Index([], dtype='int64', name='idx')

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on3-right-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'right', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on3-right-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'right', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on3-outer-disk] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'outer', shuffle_method = 'disk'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0   0.0
1   0.0   3.0   4.0
2   0.0   7.0   6.0
3   0.0   9.0  11.0
4   0.0  14.0  14.... NaN   NaN  28.0
62  7.0  31.0   NaN
63  NaN   NaN  52.0
64  NaN   NaN  29.0
65  8.0  32.0   NaN

[66 rows x 3 columns]
key = 'k'
value = 0     0
1     0
2     0
3     0
4     0
     ..
61    7
62    7
63    7
64    8
65    8
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_merge_unknown_to_unknown[on3-outer-tasks] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
ddf_right_unknown = Dask DataFrame Structure:
                    k     v1
npartitions=10              
                int64  int64
     ...           ...    ...
                  ...    ...
Dask Name: return_input, 2 expressions
Expr=ClearDivisions(frame=df)
on = ['k', 'idx'], how = 'outer', shuffle_method = 'tasks'

    def test_merge_unknown_to_unknown(
        df_left,
        df_right,
        ddf_left_unknown,
        ddf_right_unknown,
        on,
        how,
        shuffle_method,
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0   0.0
1   0.0   3.0   4.0
2   0.0   7.0   6.0
3   0.0   9.0  11.0
4   0.0  14.0  14.... NaN   NaN  28.0
62  7.0  31.0   NaN
63  NaN   NaN  52.0
64  NaN   NaN  29.0
65  8.0  32.0   NaN

[66 rows x 3 columns]
key = 'k'
value = 0     0
1     0
2     0
3     0
4     0
     ..
61    7
62    7
63    7
64    8
65    8
Length: 66, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_join[tasks-right] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', shuffle_method = 'tasks'

    @pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
    @pytest.mark.parametrize("shuffle_method", ["tasks", "disk"])
    def test_join(how, shuffle_method):
        # Make simple left & right dfs
        pdf1 = pd.DataFrame({"x": range(20), "y": range(20)})
        df1 = dd.from_pandas(pdf1, 4)
        pdf2 = pd.DataFrame({"z": range(10)}, index=pd.Index(range(10), name="a"))
        df2 = dd.from_pandas(pdf2, 2)
    
        # Partition-wise merge with map_partitions
>       df3 = df1.join(
            df2.clear_divisions(), on="x", how=how, shuffle_method=shuffle_method
        )

dask/dataframe/tests/test_merge_column_and_index.py:209: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3004: in join
    return self.merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2951: in merge
    return merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5616: in merge
    result = new_collection(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_merge.py:202: in _meta
    return make_meta(left.merge(right, **kwargs))
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        x    y  z
NaN  NaN  NaN  1
0.0  1.0  1.0  1
1.0  1.0  1.0  1
key = 'x', value = NaN    0
0.0    1
1.0    1
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[disk-cumsum-a-a] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_join[tasks-outer] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'outer', shuffle_method = 'tasks'

    @pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
    @pytest.mark.parametrize("shuffle_method", ["tasks", "disk"])
    def test_join(how, shuffle_method):
        # Make simple left & right dfs
        pdf1 = pd.DataFrame({"x": range(20), "y": range(20)})
        df1 = dd.from_pandas(pdf1, 4)
        pdf2 = pd.DataFrame({"z": range(10)}, index=pd.Index(range(10), name="a"))
        df2 = dd.from_pandas(pdf2, 2)
    
        # Partition-wise merge with map_partitions
>       df3 = df1.join(
            df2.clear_divisions(), on="x", how=how, shuffle_method=shuffle_method
        )

dask/dataframe/tests/test_merge_column_and_index.py:209: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3004: in join
    return self.merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2951: in merge
    return merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5616: in merge
    result = new_collection(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_merge.py:202: in _meta
    return make_meta(left.merge(right, **kwargs))
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        x    y  z
NaN  NaN  NaN  1
0.0  1.0  1.0  1
1.0  1.0  1.0  1
key = 'x', value = NaN    0
0.0    1
1.0    1
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[disk-cumsum-a-c] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_join[disk-right] _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', shuffle_method = 'disk'

    @pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
    @pytest.mark.parametrize("shuffle_method", ["tasks", "disk"])
    def test_join(how, shuffle_method):
        # Make simple left & right dfs
        pdf1 = pd.DataFrame({"x": range(20), "y": range(20)})
        df1 = dd.from_pandas(pdf1, 4)
        pdf2 = pd.DataFrame({"z": range(10)}, index=pd.Index(range(10), name="a"))
        df2 = dd.from_pandas(pdf2, 2)
    
        # Partition-wise merge with map_partitions
>       df3 = df1.join(
            df2.clear_divisions(), on="x", how=how, shuffle_method=shuffle_method
        )

dask/dataframe/tests/test_merge_column_and_index.py:209: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3004: in join
    return self.merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2951: in merge
    return merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5616: in merge
    result = new_collection(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_merge.py:202: in _meta
    return make_meta(left.merge(right, **kwargs))
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        x    y  z
NaN  NaN  NaN  1
0.0  1.0  1.0  1
1.0  1.0  1.0  1
key = 'x', value = NaN    0
0.0    1
1.0    1
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_cumulative[disk-cumsum-a-d] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_join[disk-outer] _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'outer', shuffle_method = 'disk'

    @pytest.mark.parametrize("how", ["left", "right", "inner", "outer"])
    @pytest.mark.parametrize("shuffle_method", ["tasks", "disk"])
    def test_join(how, shuffle_method):
        # Make simple left & right dfs
        pdf1 = pd.DataFrame({"x": range(20), "y": range(20)})
        df1 = dd.from_pandas(pdf1, 4)
        pdf2 = pd.DataFrame({"z": range(10)}, index=pd.Index(range(10), name="a"))
        df2 = dd.from_pandas(pdf2, 2)
    
        # Partition-wise merge with map_partitions
>       df3 = df1.join(
            df2.clear_divisions(), on="x", how=how, shuffle_method=shuffle_method
        )

dask/dataframe/tests/test_merge_column_and_index.py:209: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3004: in join
    return self.merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2951: in merge
    return merge(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:5616: in merge
    result = new_collection(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_merge.py:202: in _meta
    return make_meta(left.merge(right, **kwargs))
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        x    y  z
NaN  NaN  NaN  1
0.0  1.0  1.0  1
1.0  1.0  1.0  1
key = 'x', value = NaN    0
0.0    1
1.0    1
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[disk-cumsum-a-sel3] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[disk-cumsum-a-sel4] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = 'a', sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[disk-cumsum-key1-a] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[disk-cumsum-key1-c] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[disk-cumsum-key1-d] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[disk-cumsum-key1-sel3] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_cumulative[disk-cumsum-key1-sel4] ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum', key = ['a', 'b'], sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_merge_known_to_double_bcast_left[on2-disk-True-right] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_double = Dask DataFrame Structure:
                   k     v1
npartitions=2              
0              int64  int64
7                ...    ...
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], shuffle_method = 'disk', how = 'right', broadcast = True

    @pytest.mark.parametrize("how", ["inner", "right"])
    @pytest.mark.parametrize("broadcast", [True, 0.75])
    def test_merge_known_to_double_bcast_left(
        df_left, df_right, ddf_left_double, ddf_right, on, shuffle_method, how, broadcast
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:243: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[disk-cumprod-a-a] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = 'a'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_merge_known_to_double_bcast_left[on2-disk-0.75-right] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_double = Dask DataFrame Structure:
                   k     v1
npartitions=2              
0              int64  int64
7                ...    ...
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], shuffle_method = 'disk', how = 'right', broadcast = 0.75

    @pytest.mark.parametrize("how", ["inner", "right"])
    @pytest.mark.parametrize("broadcast", [True, 0.75])
    def test_merge_known_to_double_bcast_left(
        df_left, df_right, ddf_left_double, ddf_right, on, shuffle_method, how, broadcast
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:243: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[disk-cumprod-a-c] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = 'c'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_merge_known_to_double_bcast_left[on2-tasks-True-right] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_double = Dask DataFrame Structure:
                   k     v1
npartitions=2              
0              int64  int64
7                ...    ...
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], shuffle_method = 'tasks', how = 'right', broadcast = True

    @pytest.mark.parametrize("how", ["inner", "right"])
    @pytest.mark.parametrize("broadcast", [True, 0.75])
    def test_merge_known_to_double_bcast_left(
        df_left, df_right, ddf_left_double, ddf_right, on, shuffle_method, how, broadcast
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:243: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_cumulative[disk-cumprod-a-d] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = 'd'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_merge_known_to_double_bcast_left[on2-tasks-0.75-right] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_double = Dask DataFrame Structure:
                   k     v1
npartitions=2              
0              int64  int64
7                ...    ...
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['idx', 'k'], shuffle_method = 'tasks', how = 'right', broadcast = 0.75

    @pytest.mark.parametrize("how", ["inner", "right"])
    @pytest.mark.parametrize("broadcast", [True, 0.75])
    def test_merge_known_to_double_bcast_left(
        df_left, df_right, ddf_left_double, ddf_right, on, shuffle_method, how, broadcast
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:243: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     idx    k  v1_x  v1_y
0     0  0.0   0.0     0
1     0  1.0   1.0     1
2     0  2.0   2.0     2
3     0  NaN   NaN...10  3.0  47.0    48
49   10  NaN   NaN    49
50   10  NaN   NaN    50
51   10  NaN   NaN    51
52   10  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[disk-cumprod-a-sel3] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = ['a', 'b']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_merge_known_to_double_bcast_left[on3-disk-True-right] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_double = Dask DataFrame Structure:
                   k     v1
npartitions=2              
0              int64  int64
7                ...    ...
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], shuffle_method = 'disk', how = 'right', broadcast = True

    @pytest.mark.parametrize("how", ["inner", "right"])
    @pytest.mark.parametrize("broadcast", [True, 0.75])
    def test_merge_known_to_double_bcast_left(
        df_left, df_right, ddf_left_double, ddf_right, on, shuffle_method, how, broadcast
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:243: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________ test_merge_known_to_double_bcast_left[on3-disk-0.75-right] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_double = Dask DataFrame Structure:
                   k     v1
npartitions=2              
0              int64  int64
7                ...    ...
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], shuffle_method = 'disk', how = 'right', broadcast = 0.75

    @pytest.mark.parametrize("how", ["inner", "right"])
    @pytest.mark.parametrize("broadcast", [True, 0.75])
    def test_merge_known_to_double_bcast_left(
        df_left, df_right, ddf_left_double, ddf_right, on, shuffle_method, how, broadcast
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:243: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_cumulative[disk-cumprod-a-sel4] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod', key = 'a', sel = ['c', 'd']

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]])
    @pytest.mark.parametrize("key", ["a", ["a", "b"]])
    @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"])
    def test_cumulative(func, key, sel):
        df = pd.DataFrame(
            {
                "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6,
                "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6,
                "c": np.random.randn(54),
                "d": np.random.randn(54),
            },
            columns=["a", "b", "c", "d"],
        )
        df.iloc[[-18, -12, -6], -1] = np.nan
        ddf = dd.from_pandas(df, npartitions=10)
    
        g, dg = (d.groupby(key)[sel] for d in (df, ddf))
>       assert_eq(getattr(g, func)(), getattr(dg, func)())

dask/dataframe/tests/test_groupby.py:1381: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [c, d]
Index: [], key = '_by_a'
value = Series([], Name: a, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_merge_known_to_double_bcast_left[on3-tasks-True-right] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_double = Dask DataFrame Structure:
                   k     v1
npartitions=2              
0              int64  int64
7                ...    ...
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], shuffle_method = 'tasks', how = 'right', broadcast = True

    @pytest.mark.parametrize("how", ["inner", "right"])
    @pytest.mark.parametrize("broadcast", [True, 0.75])
    def test_merge_known_to_double_bcast_left(
        df_left, df_right, ddf_left_double, ddf_right, on, shuffle_method, how, broadcast
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:243: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'min', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________ test_merge_known_to_double_bcast_left[on3-tasks-0.75-right] __________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df_left =      k  v1
idx       
0    0   0
0    1   1
0    2   2
1    0   3
1    1   4
1    2   5
1    3   6
2    0   7
2    1  ...    0  37
9    1  38
9    2  39
9    3  40
9    4  41
9    5  42
9    6  43
10   0  44
10   1  45
10   2  46
10   3  47
df_right =      k  v1
idx       
0    0   0
0    1   1
0    2   2
0    3   3
1    0   4
1    1   5
2    0   6
2    1   7
2    2  ...    1  42
9    2  43
9    3  44
10   0  45
10   1  46
10   2  47
10   3  48
10   4  49
10   5  50
10   6  51
10   7  52
ddf_left_double = Dask DataFrame Structure:
                   k     v1
npartitions=2              
0              int64  int64
7                ...    ...
10               ...    ...
Dask Name: frompandas, 1 expression
Expr=df
ddf_right = Dask DataFrame Structure:
                    k     v1
npartitions=10              
0               int64  int64
1    ...     ...    ...
10                ...    ...
11                ...    ...
Dask Name: from_pd_divs, 1 expression
Expr=df
on = ['k', 'idx'], shuffle_method = 'tasks', how = 'right', broadcast = 0.75

    @pytest.mark.parametrize("how", ["inner", "right"])
    @pytest.mark.parametrize("broadcast", [True, 0.75])
    def test_merge_known_to_double_bcast_left(
        df_left, df_right, ddf_left_double, ddf_right, on, shuffle_method, how, broadcast
    ):
        # Compute expected
>       expected = df_left.merge(df_right, on=on, how=how)

dask/dataframe/tests/test_merge_column_and_index.py:243: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =       k  v1_x  v1_y
0   0.0   0.0     0
1   1.0   1.0     1
2   2.0   2.0     2
3   NaN   NaN     3
4   0.0   3.0     ...47  2.0  46.0    47
48  3.0  47.0    48
49  NaN   NaN    49
50  NaN   NaN    50
51  NaN   NaN    51
52  NaN   NaN    52
key = 'k'
value = 0     0
1     1
2     2
3     3
4     0
5     1
6     0
7     1
8     2
9     3
10    4
11    0
12    1
13    2
14    ... 5
40    6
41    0
42    1
43    2
44    3
45    0
46    1
47    2
48    3
49    4
50    5
51    6
52    7
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'min', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_assign_not_modifying_array_inplace ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_assign_not_modifying_array_inplace():
        df = pd.DataFrame({"a": [1, 2, 3], "b": 1.5})
>       result = methods.assign(df, "a", 5)

dask/dataframe/tests/test_methods.py:11: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/methods.py:348: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a    b
0  1  1.5
1  2  1.5
2  3  1.5, key = 'a', value = 5

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'min'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'min'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'min', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'min', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'min'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'min'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'min', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'min', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'min'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'min'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'min', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'min', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'min'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'min'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'max', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'max', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'max'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'max'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_merge_asof_with_empty __________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_merge_asof_with_empty():
        good_df = pd.DataFrame({"index_col": list(range(10)), "good_val": list(range(10))})
        good_dd = dd.from_pandas(good_df, npartitions=2)
        empty_df = (
            good_df[good_df.index_col < 0].copy().rename(columns={"good_val": "empty_val"})
        )
        empty_dd = dd.from_pandas(empty_df, npartitions=2)
    
        # left good, right empty
        result_dd = dd.merge_asof(
            good_dd.set_index("index_col"),
            empty_dd.set_index("index_col"),
            left_index=True,
            right_index=True,
        )
        result_df = pd.merge_asof(
            good_df.set_index("index_col"),
            empty_df.set_index("index_col"),
            left_index=True,
            right_index=True,
        )
>       assert_eq(result_dd, result_df, check_index=False)

dask/dataframe/tests/test_multi.py:474: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [index_col, empty_val]
Index: []
key = '_partitions', value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'max', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'max', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'max'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_concat_with_operation_remains_hlg ____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_concat_with_operation_remains_hlg():
        pdf1 = pd.DataFrame(
            {"x": [1, 2, 3, 4, 6, 7], "y": list("abcdef")}, index=[1, 2, 3, 4, 6, 7]
        )
        ddf1 = dd.from_pandas(pdf1, 2)
    
        pdf2 = pd.DataFrame({"y": list("abcdef")}, index=[8, 9, 10, 11, 12, 13])
        ddf2 = dd.from_pandas(pdf2, 2)
    
        # Do some operation which will remain blockwise in the dask case.
>       pdf2["x"] = range(len(pdf2["y"]))

dask/dataframe/tests/test_multi.py:627: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     y
8   a
9   b
10  c
11  d
12  e
13  f, key = 'x', value = range(0, 6)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'max'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_merge_columns_dtypes[True-inner] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'inner', on_index = True

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    @pytest.mark.parametrize("on_index", [True, False])
    def test_merge_columns_dtypes(how, on_index):
        # tests results of merges with merge columns having different dtypes;
        # asserts that either the merge was successful or the corresponding warning is raised
        # addresses issue #4574
    
        df1 = pd.DataFrame(
            {"A": list(np.arange(5).astype(float)) * 2, "B": list(np.arange(5)) * 2}
        )
        df2 = pd.DataFrame({"A": np.arange(5), "B": np.arange(5)})
    
        a = dd.from_pandas(df1, 2)  # merge column "A" is float
        b = dd.from_pandas(df2, 2)  # merge column "A" is int
    
        on = ["A"]
        left_index = right_index = on_index
    
        if on_index:
            a = a.set_index("A")
            b = b.set_index("A")
            on = None
    
        with warnings.catch_warnings(record=True) as record:
            warnings.simplefilter("always")
            result = dd.merge(
                a, b, on=on, how=how, left_index=left_index, right_index=right_index
            )
            warned = any("merge column data type mismatches" in str(r) for r in record)
    
        # result type depends on merge operation -> convert to pandas
>       result = result if isinstance(result, pd.DataFrame) else result.compute()

dask/dataframe/tests/test_multi.py:693: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'max', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_merge_columns_dtypes[True-outer] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'outer', on_index = True

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    @pytest.mark.parametrize("on_index", [True, False])
    def test_merge_columns_dtypes(how, on_index):
        # tests results of merges with merge columns having different dtypes;
        # asserts that either the merge was successful or the corresponding warning is raised
        # addresses issue #4574
    
        df1 = pd.DataFrame(
            {"A": list(np.arange(5).astype(float)) * 2, "B": list(np.arange(5)) * 2}
        )
        df2 = pd.DataFrame({"A": np.arange(5), "B": np.arange(5)})
    
        a = dd.from_pandas(df1, 2)  # merge column "A" is float
        b = dd.from_pandas(df2, 2)  # merge column "A" is int
    
        on = ["A"]
        left_index = right_index = on_index
    
        if on_index:
            a = a.set_index("A")
            b = b.set_index("A")
            on = None
    
        with warnings.catch_warnings(record=True) as record:
            warnings.simplefilter("always")
            result = dd.merge(
                a, b, on=on, how=how, left_index=left_index, right_index=right_index
            )
            warned = any("merge column data type mismatches" in str(r) for r in record)
    
        # result type depends on merge operation -> convert to pandas
>       result = result if isinstance(result, pd.DataFrame) else result.compute()

dask/dataframe/tests/test_multi.py:693: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'max', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_merge_columns_dtypes[True-left] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'left', on_index = True

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    @pytest.mark.parametrize("on_index", [True, False])
    def test_merge_columns_dtypes(how, on_index):
        # tests results of merges with merge columns having different dtypes;
        # asserts that either the merge was successful or the corresponding warning is raised
        # addresses issue #4574
    
        df1 = pd.DataFrame(
            {"A": list(np.arange(5).astype(float)) * 2, "B": list(np.arange(5)) * 2}
        )
        df2 = pd.DataFrame({"A": np.arange(5), "B": np.arange(5)})
    
        a = dd.from_pandas(df1, 2)  # merge column "A" is float
        b = dd.from_pandas(df2, 2)  # merge column "A" is int
    
        on = ["A"]
        left_index = right_index = on_index
    
        if on_index:
            a = a.set_index("A")
            b = b.set_index("A")
            on = None
    
        with warnings.catch_warnings(record=True) as record:
            warnings.simplefilter("always")
            result = dd.merge(
                a, b, on=on, how=how, left_index=left_index, right_index=right_index
            )
            warned = any("merge column data type mismatches" in str(r) for r in record)
    
        # result type depends on merge operation -> convert to pandas
>       result = result if isinstance(result, pd.DataFrame) else result.compute()

dask/dataframe/tests/test_multi.py:693: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'max'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_merge_columns_dtypes[True-right] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', on_index = True

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    @pytest.mark.parametrize("on_index", [True, False])
    def test_merge_columns_dtypes(how, on_index):
        # tests results of merges with merge columns having different dtypes;
        # asserts that either the merge was successful or the corresponding warning is raised
        # addresses issue #4574
    
        df1 = pd.DataFrame(
            {"A": list(np.arange(5).astype(float)) * 2, "B": list(np.arange(5)) * 2}
        )
        df2 = pd.DataFrame({"A": np.arange(5), "B": np.arange(5)})
    
        a = dd.from_pandas(df1, 2)  # merge column "A" is float
        b = dd.from_pandas(df2, 2)  # merge column "A" is int
    
        on = ["A"]
        left_index = right_index = on_index
    
        if on_index:
            a = a.set_index("A")
            b = b.set_index("A")
            on = None
    
        with warnings.catch_warnings(record=True) as record:
            warnings.simplefilter("always")
            result = dd.merge(
                a, b, on=on, how=how, left_index=left_index, right_index=right_index
            )
            warned = any("merge column data type mismatches" in str(r) for r in record)
    
        # result type depends on merge operation -> convert to pandas
>       result = result if isinstance(result, pd.DataFrame) else result.compute()

dask/dataframe/tests/test_multi.py:693: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'max'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'max', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_merge[disk-outer] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'outer', shuffle_method = 'disk'

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    def test_merge(how, shuffle_method):
        A = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6], "y": [1, 1, 2, 2, 3, 4]})
        a = dd.repartition(A, [0, 4, 5])
    
        B = pd.DataFrame({"y": [1, 3, 4, 4, 5, 6], "z": [6, 5, 4, 3, 2, 1]})
        b = dd.repartition(B, [0, 2, 5])
    
        assert_eq(
            dd.merge(
                a,
                b,
                left_index=True,
                right_index=True,
                how=how,
                shuffle_method=shuffle_method,
            ),
            pd.merge(A, B, left_index=True, right_index=True, how=how),
        )
    
        result = dd.merge(a, b, on="y", how=how)
>       list_eq(result, pd.merge(A, B, on="y", how=how))

dask/dataframe/tests/test_multi.py:720: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x    y    z
0  1.0  1.0  6.0
1  2.0  1.0  6.0
2  3.0  2.0  NaN
3  4.0  2.0  NaN
4  5.0  3.0  5.0
5  6.0  4.0  4.0
6  6.0  4.0  3.0
7  NaN  NaN  2.0
8  NaN  NaN  1.0
key = 'y'
value = 0    1
1    1
2    2
3    2
4    3
5    4
6    4
7    5
8    6
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'max', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_merge[disk-right] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', shuffle_method = 'disk'

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    def test_merge(how, shuffle_method):
        A = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6], "y": [1, 1, 2, 2, 3, 4]})
        a = dd.repartition(A, [0, 4, 5])
    
        B = pd.DataFrame({"y": [1, 3, 4, 4, 5, 6], "z": [6, 5, 4, 3, 2, 1]})
        b = dd.repartition(B, [0, 2, 5])
    
        assert_eq(
            dd.merge(
                a,
                b,
                left_index=True,
                right_index=True,
                how=how,
                shuffle_method=shuffle_method,
            ),
            pd.merge(A, B, left_index=True, right_index=True, how=how),
        )
    
        result = dd.merge(a, b, on="y", how=how)
>       list_eq(result, pd.merge(A, B, on="y", how=how))

dask/dataframe/tests/test_multi.py:720: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x    y  z
0  1.0  1.0  6
1  2.0  1.0  6
2  5.0  3.0  5
3  6.0  4.0  4
4  6.0  4.0  3
5  NaN  NaN  2
6  NaN  NaN  1
key = 'y', value = 0    1
1    1
2    3
3    4
4    4
5    5
6    6
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'max'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'max'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_merge[tasks-outer] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'outer', shuffle_method = 'tasks'

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    def test_merge(how, shuffle_method):
        A = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6], "y": [1, 1, 2, 2, 3, 4]})
        a = dd.repartition(A, [0, 4, 5])
    
        B = pd.DataFrame({"y": [1, 3, 4, 4, 5, 6], "z": [6, 5, 4, 3, 2, 1]})
        b = dd.repartition(B, [0, 2, 5])
    
        assert_eq(
            dd.merge(
                a,
                b,
                left_index=True,
                right_index=True,
                how=how,
                shuffle_method=shuffle_method,
            ),
            pd.merge(A, B, left_index=True, right_index=True, how=how),
        )
    
        result = dd.merge(a, b, on="y", how=how)
>       list_eq(result, pd.merge(A, B, on="y", how=how))

dask/dataframe/tests/test_multi.py:720: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x    y    z
0  1.0  1.0  6.0
1  2.0  1.0  6.0
2  3.0  2.0  NaN
3  4.0  2.0  NaN
4  5.0  3.0  5.0
5  6.0  4.0  4.0
6  6.0  4.0  3.0
7  NaN  NaN  2.0
8  NaN  NaN  1.0
key = 'y'
value = 0    1
1    1
2    2
3    2
4    3
5    4
6    4
7    5
8    6
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'count', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_merge[tasks-right] ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', shuffle_method = 'tasks'

    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
    def test_merge(how, shuffle_method):
        A = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6], "y": [1, 1, 2, 2, 3, 4]})
        a = dd.repartition(A, [0, 4, 5])
    
        B = pd.DataFrame({"y": [1, 3, 4, 4, 5, 6], "z": [6, 5, 4, 3, 2, 1]})
        b = dd.repartition(B, [0, 2, 5])
    
        assert_eq(
            dd.merge(
                a,
                b,
                left_index=True,
                right_index=True,
                how=how,
                shuffle_method=shuffle_method,
            ),
            pd.merge(A, B, left_index=True, right_index=True, how=how),
        )
    
        result = dd.merge(a, b, on="y", how=how)
>       list_eq(result, pd.merge(A, B, on="y", how=how))

dask/dataframe/tests/test_multi.py:720: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      x    y  z
0  1.0  1.0  6
1  2.0  1.0  6
2  5.0  3.0  5
3  6.0  4.0  4
4  6.0  4.0  3
5  NaN  NaN  2
6  NaN  NaN  1
key = 'y', value = 0    1
1    1
2    3
3    4
4    4
5    5
6    6
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_merge_empty_left_df[disk-right] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'disk', how = 'right'

    @pytest.mark.parametrize("how", ["right", "outer"])
    def test_merge_empty_left_df(shuffle_method, how):
        # We're merging on "a", but in a situation where the left
        # frame is missing some values, so some of the blocked merges
        # will involve an empty left frame.
        # https://github.com/pandas-dev/pandas/issues/9937
        left = pd.DataFrame({"a": [1, 1, 2, 2], "val": [5, 6, 7, 8]})
        right = pd.DataFrame({"a": [0, 0, 3, 3], "val": [11, 12, 13, 14]})
    
        dd_left = dd.from_pandas(left, npartitions=4)
        dd_right = dd.from_pandas(right, npartitions=4)
    
        merged = dd_left.merge(dd_right, on="a", how=how)
>       expected = left.merge(right, on="a", how=how)

dask/dataframe/tests/test_multi.py:816: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     a  val_x  val_y
0 NaN    NaN     11
1 NaN    NaN     12
2 NaN    NaN     13
3 NaN    NaN     14
key = 'a', value = 0    0
1    0
2    3
3    3
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'count', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_merge_empty_left_df[disk-outer] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'disk', how = 'outer'

    @pytest.mark.parametrize("how", ["right", "outer"])
    def test_merge_empty_left_df(shuffle_method, how):
        # We're merging on "a", but in a situation where the left
        # frame is missing some values, so some of the blocked merges
        # will involve an empty left frame.
        # https://github.com/pandas-dev/pandas/issues/9937
        left = pd.DataFrame({"a": [1, 1, 2, 2], "val": [5, 6, 7, 8]})
        right = pd.DataFrame({"a": [0, 0, 3, 3], "val": [11, 12, 13, 14]})
    
        dd_left = dd.from_pandas(left, npartitions=4)
        dd_right = dd.from_pandas(right, npartitions=4)
    
        merged = dd_left.merge(dd_right, on="a", how=how)
>       expected = left.merge(right, on="a", how=how)

dask/dataframe/tests/test_multi.py:816: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      a  val_x  val_y
0  NaN    NaN   11.0
1  NaN    NaN   12.0
2  1.0    5.0    NaN
3  1.0    6.0    NaN
4  2.0    7.0    NaN
5  2.0    8.0    NaN
6  NaN    NaN   13.0
7  NaN    NaN   14.0
key = 'a'
value = 0    0
1    0
2    1
3    1
4    2
5    2
6    3
7    3
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'count', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_merge_empty_left_df[tasks-right] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks', how = 'right'

    @pytest.mark.parametrize("how", ["right", "outer"])
    def test_merge_empty_left_df(shuffle_method, how):
        # We're merging on "a", but in a situation where the left
        # frame is missing some values, so some of the blocked merges
        # will involve an empty left frame.
        # https://github.com/pandas-dev/pandas/issues/9937
        left = pd.DataFrame({"a": [1, 1, 2, 2], "val": [5, 6, 7, 8]})
        right = pd.DataFrame({"a": [0, 0, 3, 3], "val": [11, 12, 13, 14]})
    
        dd_left = dd.from_pandas(left, npartitions=4)
        dd_right = dd.from_pandas(right, npartitions=4)
    
        merged = dd_left.merge(dd_right, on="a", how=how)
>       expected = left.merge(right, on="a", how=how)

dask/dataframe/tests/test_multi.py:816: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =     a  val_x  val_y
0 NaN    NaN     11
1 NaN    NaN     12
2 NaN    NaN     13
3 NaN    NaN     14
key = 'a', value = 0    0
1    0
2    3
3    3
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_merge_empty_left_df[tasks-outer] _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks', how = 'outer'

    @pytest.mark.parametrize("how", ["right", "outer"])
    def test_merge_empty_left_df(shuffle_method, how):
        # We're merging on "a", but in a situation where the left
        # frame is missing some values, so some of the blocked merges
        # will involve an empty left frame.
        # https://github.com/pandas-dev/pandas/issues/9937
        left = pd.DataFrame({"a": [1, 1, 2, 2], "val": [5, 6, 7, 8]})
        right = pd.DataFrame({"a": [0, 0, 3, 3], "val": [11, 12, 13, 14]})
    
        dd_left = dd.from_pandas(left, npartitions=4)
        dd_right = dd.from_pandas(right, npartitions=4)
    
        merged = dd_left.merge(dd_right, on="a", how=how)
>       expected = left.merge(right, on="a", how=how)

dask/dataframe/tests/test_multi.py:816: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/frame.py:10832: in merge
    return merge(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =      a  val_x  val_y
0  NaN    NaN   11.0
1  NaN    NaN   12.0
2  1.0    5.0    NaN
3  1.0    6.0    NaN
4  2.0    7.0    NaN
5  2.0    8.0    NaN
6  NaN    NaN   13.0
7  NaN    NaN   14.0
key = 'a'
value = 0    0
1    0
2    1
3    1
4    2
5    2
6    3
7    3
dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'count', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'count', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'count', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'count', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'count', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'size', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'size', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'size'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'size'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'size', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'size', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'size'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'size'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'size', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'size', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'size'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'size'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'size', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'size', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'size'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'size'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_concat_categorical[False-True-False] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

known = False, cat_index = True, divisions = False

    @pytest.mark.parametrize(
        "known, cat_index, divisions",
        [
            (True, True, False),
            pytest.param(
                True,
                False,
                True,
                marks=pytest.mark.xfail(
                    PANDAS_GE_220 or PY_VERSION >= Version("3.12.0"),
                    reason="fails on pandas dev: https://github.com/dask/dask/issues/10558",
                    raises=AssertionError,
                    strict=False,
                ),
            ),
            (True, False, False),
            (False, True, False),
            pytest.param(
                False,
                False,
                True,
                marks=pytest.mark.xfail(
                    PANDAS_GE_220 or PY_VERSION >= Version("3.12.0"),
                    reason="fails on pandas dev: https://github.com/dask/dask/issues/10558",
                    raises=AssertionError,
                    strict=False,
                ),
            ),
            (False, False, False),
        ],
    )
    def test_concat_categorical(known, cat_index, divisions):
        frames = [
            pd.DataFrame(
                {
                    "w": list("xxxxx"),
                    "x": np.arange(5),
                    "y": list("abcbc"),
                    "z": np.arange(5, dtype="f8"),
                }
            ),
            pd.DataFrame(
                {
                    "w": list("yyyyy"),
                    "x": np.arange(5, 10),
                    "y": list("abbba"),
                    "z": np.arange(5, 10, dtype="f8"),
                }
            ),
            pd.DataFrame(
                {
                    "w": list("zzzzz"),
                    "x": np.arange(10, 15),
                    "y": list("bcbcc"),
                    "z": np.arange(10, 15, dtype="f8"),
                }
            ),
        ]
        for df in frames:
            df.w = df.w.astype("category")
            df.y = df.y.astype("category")
    
        if cat_index:
            frames = [df.set_index(df.y) for df in frames]
    
        dframes = [dd.from_pandas(p, npartitions=2, sort=divisions) for p in frames]
    
        if not known:
>           dframes[0]["y"] = dframes[0]["y"].cat.as_unknown()

dask/dataframe/tests/test_multi.py:1975: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [w, x, y, z]
Index: [], key = 'y'
value = Series([], Name: y, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_concat_categorical[False-False-True] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

known = False, cat_index = False, divisions = True

    @pytest.mark.parametrize(
        "known, cat_index, divisions",
        [
            (True, True, False),
            pytest.param(
                True,
                False,
                True,
                marks=pytest.mark.xfail(
                    PANDAS_GE_220 or PY_VERSION >= Version("3.12.0"),
                    reason="fails on pandas dev: https://github.com/dask/dask/issues/10558",
                    raises=AssertionError,
                    strict=False,
                ),
            ),
            (True, False, False),
            (False, True, False),
            pytest.param(
                False,
                False,
                True,
                marks=pytest.mark.xfail(
                    PANDAS_GE_220 or PY_VERSION >= Version("3.12.0"),
                    reason="fails on pandas dev: https://github.com/dask/dask/issues/10558",
                    raises=AssertionError,
                    strict=False,
                ),
            ),
            (False, False, False),
        ],
    )
    def test_concat_categorical(known, cat_index, divisions):
        frames = [
            pd.DataFrame(
                {
                    "w": list("xxxxx"),
                    "x": np.arange(5),
                    "y": list("abcbc"),
                    "z": np.arange(5, dtype="f8"),
                }
            ),
            pd.DataFrame(
                {
                    "w": list("yyyyy"),
                    "x": np.arange(5, 10),
                    "y": list("abbba"),
                    "z": np.arange(5, 10, dtype="f8"),
                }
            ),
            pd.DataFrame(
                {
                    "w": list("zzzzz"),
                    "x": np.arange(10, 15),
                    "y": list("bcbcc"),
                    "z": np.arange(10, 15, dtype="f8"),
                }
            ),
        ]
        for df in frames:
            df.w = df.w.astype("category")
            df.y = df.y.astype("category")
    
        if cat_index:
            frames = [df.set_index(df.y) for df in frames]
    
        dframes = [dd.from_pandas(p, npartitions=2, sort=divisions) for p in frames]
    
        if not known:
>           dframes[0]["y"] = dframes[0]["y"].cat.as_unknown()

dask/dataframe/tests/test_multi.py:1975: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [w, x, y, z]
Index: [], key = 'y'
value = Series([], Name: y, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_concat_categorical[False-False-False] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

known = False, cat_index = False, divisions = False

    @pytest.mark.parametrize(
        "known, cat_index, divisions",
        [
            (True, True, False),
            pytest.param(
                True,
                False,
                True,
                marks=pytest.mark.xfail(
                    PANDAS_GE_220 or PY_VERSION >= Version("3.12.0"),
                    reason="fails on pandas dev: https://github.com/dask/dask/issues/10558",
                    raises=AssertionError,
                    strict=False,
                ),
            ),
            (True, False, False),
            (False, True, False),
            pytest.param(
                False,
                False,
                True,
                marks=pytest.mark.xfail(
                    PANDAS_GE_220 or PY_VERSION >= Version("3.12.0"),
                    reason="fails on pandas dev: https://github.com/dask/dask/issues/10558",
                    raises=AssertionError,
                    strict=False,
                ),
            ),
            (False, False, False),
        ],
    )
    def test_concat_categorical(known, cat_index, divisions):
        frames = [
            pd.DataFrame(
                {
                    "w": list("xxxxx"),
                    "x": np.arange(5),
                    "y": list("abcbc"),
                    "z": np.arange(5, dtype="f8"),
                }
            ),
            pd.DataFrame(
                {
                    "w": list("yyyyy"),
                    "x": np.arange(5, 10),
                    "y": list("abbba"),
                    "z": np.arange(5, 10, dtype="f8"),
                }
            ),
            pd.DataFrame(
                {
                    "w": list("zzzzz"),
                    "x": np.arange(10, 15),
                    "y": list("bcbcc"),
                    "z": np.arange(10, 15, dtype="f8"),
                }
            ),
        ]
        for df in frames:
            df.w = df.w.astype("category")
            df.y = df.y.astype("category")
    
        if cat_index:
            frames = [df.set_index(df.y) for df in frames]
    
        dframes = [dd.from_pandas(p, npartitions=2, sort=divisions) for p in frames]
    
        if not known:
>           dframes[0]["y"] = dframes[0]["y"].cat.as_unknown()

dask/dataframe/tests/test_multi.py:1975: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [w, x, y, z]
Index: [], key = 'y'
value = Series([], Name: y, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_concat_categorical_mixed_simple _____________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_concat_categorical_mixed_simple():
        a = pd.Series(["a", "b", "c"], dtype="category")
        b = pd.Series(["a", "b"], dtype="category")
        da = dd.from_pandas(a, 2).cat.as_unknown().to_frame("A")
        db = dd.from_pandas(b, 2).to_frame("A")
    
        expected = concat([a.to_frame("A"), b.to_frame("A")])
        result = dd.concat([da, db])
>       assert_eq(result, expected)

dask/dataframe/tests/test_multi.py:2024: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:612: in assert_sane_keynames
    dsk = dsk.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:541: in __dask_graph__
    return out.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_concat.py:296: in _layer
    meta = strip_unknown_categories(self._meta)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:207: in strip_unknown_categories
    x[c] = x[c].cat.set_categories([])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A]
Index: [], key = 'A'
value = Series([], Name: A, dtype: category
Categories (0, object): [])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_concat_datetimeindex ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_concat_datetimeindex():
        # https://github.com/dask/dask/issues/2932
        b2 = pd.DataFrame(
            {"x": ["a"]},
            index=pd.DatetimeIndex(["2015-03-24 00:00:16"], dtype="datetime64[ns]"),
        )
        b3 = pd.DataFrame(
            {"x": ["c"]},
            index=pd.DatetimeIndex(["2015-03-29 00:00:44"], dtype="datetime64[ns]"),
        )
    
>       b2["x"] = b2.x.astype("category").cat.set_categories(["a", "c"])

dask/dataframe/tests/test_multi.py:2038: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =                      x
2015-03-24 00:00:16  a, key = 'x'
value = 2015-03-24 00:00:16    a
Name: x, dtype: category
Categories (2, object): ['a', 'c']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_singleton_divisions ___________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_singleton_divisions():
        df = pd.DataFrame({"x": [1, 1, 1]}, index=[1, 2, 3])
        ddf = dd.from_pandas(df, npartitions=2)
        ddf2 = ddf.set_index("x")
    
        joined = ddf2.join(ddf2, rsuffix="r")
        assert joined.divisions == (1, 1)
>       joined.compute()

dask/dataframe/tests/test_multi.py:2068: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_repartition_repeated_divisions ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_repartition_repeated_divisions():
        df = pd.DataFrame({"x": [0, 0, 0, 0]})
        ddf = dd.from_pandas(df, npartitions=2).set_index("x")
    
        ddf2 = ddf.repartition(divisions=(0, 0), force=True)
>       assert_eq(ddf2, df.set_index("x"))

dask/dataframe/tests/test_multi.py:2076: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_multi_duplicate_divisions ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_multi_duplicate_divisions():
        df1 = pd.DataFrame({"x": [0, 0, 0, 0]})
        df2 = pd.DataFrame({"x": [0]})
    
        ddf1 = dd.from_pandas(df1, npartitions=2).set_index("x")
        ddf2 = dd.from_pandas(df2, npartitions=1).set_index("x")
        assert ddf1.npartitions <= 2
        assert len(ddf1) == len(df1)
    
        r1 = ddf1.merge(ddf2, how="left", left_index=True, right_index=True)
    
        sf1 = df1.set_index("x")
        sf2 = df2.set_index("x")
        r2 = sf1.merge(sf2, how="left", left_index=True, right_index=True)
    
>       assert_eq(r1, r2)

dask/dataframe/tests/test_multi.py:2094: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_concat_ignore_order[True] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

ordered = True

    @pytest.mark.parametrize("ordered", [True, False])
    def test_concat_ignore_order(ordered):
        pdf1 = pd.DataFrame(
            {
                "x": pd.Categorical(
                    ["a", "b", "c", "a"], categories=["a", "b", "c"], ordered=ordered
                )
            }
        )
        ddf1 = dd.from_pandas(pdf1, 2)
        pdf2 = pd.DataFrame(
            {
                "x": pd.Categorical(
                    ["c", "b", "a"], categories=["c", "b", "a"], ordered=ordered
                )
            }
        )
        ddf2 = dd.from_pandas(pdf2, 2)
        expected = pd.concat([pdf1, pdf2])
>       expected["x"] = expected["x"].astype("category")

dask/dataframe/tests/test_multi.py:2193: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x
0  a
1  b
2  c
3  a
0  c
1  b
2  a, key = 'x'
value = 0    a
1    b
2    c
3    a
0    c
1    b
2    a
Name: x, dtype: category
Categories (3, object): ['a', 'b', 'c']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_concat_ignore_order[False] ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

ordered = False

    @pytest.mark.parametrize("ordered", [True, False])
    def test_concat_ignore_order(ordered):
        pdf1 = pd.DataFrame(
            {
                "x": pd.Categorical(
                    ["a", "b", "c", "a"], categories=["a", "b", "c"], ordered=ordered
                )
            }
        )
        ddf1 = dd.from_pandas(pdf1, 2)
        pdf2 = pd.DataFrame(
            {
                "x": pd.Categorical(
                    ["c", "b", "a"], categories=["c", "b", "a"], ordered=ordered
                )
            }
        )
        ddf2 = dd.from_pandas(pdf2, 2)
        expected = pd.concat([pdf1, pdf2])
>       expected["x"] = expected["x"].astype("category")

dask/dataframe/tests/test_multi.py:2193: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x
0  a
1  b
2  c
3  a
0  c
1  b
2  a, key = 'x'
value = 0    a
1    b
2    c
3    a
0    c
1    b
2    a
Name: x, dtype: category
Categories (3, object): ['a', 'b', 'c']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_nullable_types_merge[Int64] _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dtype = 'Int64'

    @pytest.mark.parametrize(
        "dtype",
        [
            "Int64",
            "Float64",
            pytest.param(
                "int64[pyarrow]",
                marks=pytest.mark.skipif(
                    pa is None,
                    reason="Support for ArrowDtypes requires pyarrow and pandas>=1.5.0",
                ),
            ),
            pytest.param(
                "float64[pyarrow]",
                marks=pytest.mark.skipif(
                    pa is None,
                    reason="Support for ArrowDtypes requires pyarrow and pandas>=1.5.0",
                ),
            ),
        ],
    )
    def test_nullable_types_merge(dtype):
        df1 = pd.DataFrame({"a": [1, 2, 3], "b": [1, 1, 3], "c": list("aab")})
        df2 = pd.DataFrame({"a": [1, 2, 3], "e": [1, 1, 3], "f": list("aab")})
>       df1["a"] = df1["a"].astype(dtype)

dask/dataframe/tests/test_multi.py:2222: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c
0  1  1  a
1  2  1  a
2  3  3  b, key = 'a'
value = 0    1
1    2
2    3
Name: a, dtype: Int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_nullable_types_merge[Float64] ______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dtype = 'Float64'

    @pytest.mark.parametrize(
        "dtype",
        [
            "Int64",
            "Float64",
            pytest.param(
                "int64[pyarrow]",
                marks=pytest.mark.skipif(
                    pa is None,
                    reason="Support for ArrowDtypes requires pyarrow and pandas>=1.5.0",
                ),
            ),
            pytest.param(
                "float64[pyarrow]",
                marks=pytest.mark.skipif(
                    pa is None,
                    reason="Support for ArrowDtypes requires pyarrow and pandas>=1.5.0",
                ),
            ),
        ],
    )
    def test_nullable_types_merge(dtype):
        df1 = pd.DataFrame({"a": [1, 2, 3], "b": [1, 1, 3], "c": list("aab")})
        df2 = pd.DataFrame({"a": [1, 2, 3], "e": [1, 1, 3], "f": list("aab")})
>       df1["a"] = df1["a"].astype(dtype)

dask/dataframe/tests/test_multi.py:2222: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c
0  1  1  a
1  2  1  a
2  3  3  b, key = 'a'
value = 0    1.0
1    2.0
2    3.0
Name: a, dtype: Float64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_nullable_types_merge[int64[pyarrow]] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dtype = 'int64[pyarrow]'

    @pytest.mark.parametrize(
        "dtype",
        [
            "Int64",
            "Float64",
            pytest.param(
                "int64[pyarrow]",
                marks=pytest.mark.skipif(
                    pa is None,
                    reason="Support for ArrowDtypes requires pyarrow and pandas>=1.5.0",
                ),
            ),
            pytest.param(
                "float64[pyarrow]",
                marks=pytest.mark.skipif(
                    pa is None,
                    reason="Support for ArrowDtypes requires pyarrow and pandas>=1.5.0",
                ),
            ),
        ],
    )
    def test_nullable_types_merge(dtype):
        df1 = pd.DataFrame({"a": [1, 2, 3], "b": [1, 1, 3], "c": list("aab")})
        df2 = pd.DataFrame({"a": [1, 2, 3], "e": [1, 1, 3], "f": list("aab")})
>       df1["a"] = df1["a"].astype(dtype)

dask/dataframe/tests/test_multi.py:2222: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c
0  1  1  a
1  2  1  a
2  3  3  b, key = 'a'
value = 0    1
1    2
2    3
Name: a, dtype: int64[pyarrow]

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_nullable_types_merge[float64[pyarrow]] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

dtype = 'float64[pyarrow]'

    @pytest.mark.parametrize(
        "dtype",
        [
            "Int64",
            "Float64",
            pytest.param(
                "int64[pyarrow]",
                marks=pytest.mark.skipif(
                    pa is None,
                    reason="Support for ArrowDtypes requires pyarrow and pandas>=1.5.0",
                ),
            ),
            pytest.param(
                "float64[pyarrow]",
                marks=pytest.mark.skipif(
                    pa is None,
                    reason="Support for ArrowDtypes requires pyarrow and pandas>=1.5.0",
                ),
            ),
        ],
    )
    def test_nullable_types_merge(dtype):
        df1 = pd.DataFrame({"a": [1, 2, 3], "b": [1, 1, 3], "c": list("aab")})
        df2 = pd.DataFrame({"a": [1, 2, 3], "e": [1, 1, 3], "f": list("aab")})
>       df1["a"] = df1["a"].astype(dtype)

dask/dataframe/tests/test_multi.py:2222: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a  b  c
0  1  1  a
1  2  1  a
2  3  3  b, key = 'a'
value = 0    1.0
1    2.0
2    3.0
Name: a, dtype: double[pyarrow]

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_categorical_join _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_categorical_join():
        # https://github.com/dask/dask/issues/6134
        df = pd.DataFrame(
            {
                "join_col": ["a", "a", "b", "b"],
                "a": [0, 0, 10, 10],
            }
        )
        df2 = pd.DataFrame({"b": [1, 2, 1, 2]}, index=["a", "a", "b", "b"])
    
        ddf = dd.from_pandas(df, npartitions=2)
        ddf2 = dd.from_pandas(df2, npartitions=1)
>       ddf["join_col"] = ddf["join_col"].astype("category")

dask/dataframe/tests/test_multi.py:2249: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [join_col, a]
Index: [], key = 'join_col'
value = Series([], Name: join_col, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__ test_categorical_merge_with_merge_column_cat_in_one_and_not_other_upcasts ___
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_categorical_merge_with_merge_column_cat_in_one_and_not_other_upcasts():
        df1 = pd.DataFrame({"A": pd.Categorical([0, 1]), "B": pd.Categorical(["a", "b"])})
        df2 = pd.DataFrame({"C": pd.Categorical(["a", "b"])})
    
>       expected = pd.merge(df2, df1, left_index=True, right_on="A")

dask/dataframe/tests/test_multi.py:2283: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:170: in merge
    op = _MergeOperation(
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:807: in __init__
    self._maybe_coerce_merge_keys()
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1552: in _maybe_coerce_merge_keys
    self.right[name] = self.right[name].astype(typ)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    A  B
0  0  a
1  1  b, key = 'A'
value = 0    0
1    1
Name: A, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_categorical_merge_retains_category_dtype _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_categorical_merge_retains_category_dtype():
        # https://github.com/dask/dask/issues/6142
        a = pd.DataFrame({"A": [0, 1, 2, 3], "B": [4, 5, 6, 7]})
        b = pd.DataFrame({"A": [0, 1, 2, 4], "C": [4, 5, 7, 7]})
    
        df1 = dd.from_pandas(a, 2)
>       df1["A"] = df1.A.astype("category")

dask/dataframe/tests/test_multi.py:2302: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = 'A'
value = Series([], Name: A, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[lg-28-inner] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'inner', npartitions = 28, base = 'lg'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
        pd_result = pd.merge(left, right, on="y", how=how)
    
        # Make sure `on` dtypes match
>       dd_result["y"] = dd_result["y"].astype(np.int32)

dask/dataframe/tests/test_multi.py:2375: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x_x, y, x_y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: int32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[lg-28-left] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'left', npartitions = 28, base = 'lg'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
        pd_result = pd.merge(left, right, on="y", how=how)
    
        # Make sure `on` dtypes match
>       dd_result["y"] = dd_result["y"].astype(np.int32)

dask/dataframe/tests/test_multi.py:2375: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x_x, y, x_y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: int32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[lg-28-right] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', npartitions = 28, base = 'lg'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
        pd_result = pd.merge(left, right, on="y", how=how)
    
        # Make sure `on` dtypes match
>       dd_result["y"] = dd_result["y"].astype(np.int32)

dask/dataframe/tests/test_multi.py:2375: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x_x, y, x_y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: int32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[lg-32-inner] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'inner', npartitions = 32, base = 'lg'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
        pd_result = pd.merge(left, right, on="y", how=how)
    
        # Make sure `on` dtypes match
>       dd_result["y"] = dd_result["y"].astype(np.int32)

dask/dataframe/tests/test_multi.py:2375: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x_x, y, x_y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: int32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[lg-32-left] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'left', npartitions = 32, base = 'lg'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
        pd_result = pd.merge(left, right, on="y", how=how)
    
        # Make sure `on` dtypes match
>       dd_result["y"] = dd_result["y"].astype(np.int32)

dask/dataframe/tests/test_multi.py:2375: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x_x, y, x_y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: int32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[lg-32-right] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', npartitions = 32, base = 'lg'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
        pd_result = pd.merge(left, right, on="y", how=how)
    
        # Make sure `on` dtypes match
>       dd_result["y"] = dd_result["y"].astype(np.int32)

dask/dataframe/tests/test_multi.py:2375: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x_x, y, x_y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: int32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[sm-28-inner] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'inner', npartitions = 28, base = 'sm'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
        pd_result = pd.merge(left, right, on="y", how=how)
    
        # Make sure `on` dtypes match
>       dd_result["y"] = dd_result["y"].astype(np.int32)

dask/dataframe/tests/test_multi.py:2375: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x_x, y, x_y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: int32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[sm-28-left] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'left', npartitions = 28, base = 'sm'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
        pd_result = pd.merge(left, right, on="y", how=how)
    
        # Make sure `on` dtypes match
>       dd_result["y"] = dd_result["y"].astype(np.int32)

dask/dataframe/tests/test_multi.py:2375: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x_x, y, x_y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: int32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[sm-28-right] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', npartitions = 28, base = 'sm'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
>       pd_result = pd.merge(left, right, on="y", how=how)

dask/dataframe/tests/test_multi.py:2372: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        x_x    y  x_y
0     80.0  0.0   68
1     97.0  1.0   18
2     82.0  2.0   16
3     98.0  3.0    5
4     21.0  4...  NaN   15
2996   NaN  NaN   84
2997   NaN  NaN   33
2998   NaN  NaN   64
2999   NaN  NaN   30

[3000 rows x 3 columns]
key = 'y'
value = 0          0
1          1
2          2
3          3
4          4
        ... 
2995    2995
2996    2996
2997    2997
2998    2998
2999    2999
Length: 3000, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[sm-32-inner] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'inner', npartitions = 32, base = 'sm'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
        pd_result = pd.merge(left, right, on="y", how=how)
    
        # Make sure `on` dtypes match
>       dd_result["y"] = dd_result["y"].astype(np.int32)

dask/dataframe/tests/test_multi.py:2375: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x_x, y, x_y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: int32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'first', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[sm-32-left] __________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'left', npartitions = 32, base = 'sm'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
        pd_result = pd.merge(left, right, on="y", how=how)
    
        # Make sure `on` dtypes match
>       dd_result["y"] = dd_result["y"].astype(np.int32)

dask/dataframe/tests/test_multi.py:2375: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x_x, y, x_y]
Index: [], key = 'y'
value = Series([], Name: y, dtype: int32)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_merge_tasks_large_to_small[sm-32-right] _________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

how = 'right', npartitions = 32, base = 'sm'

    @pytest.mark.parametrize("how", ["inner", "left", "right"])
    @pytest.mark.parametrize("npartitions", [28, 32])
    @pytest.mark.parametrize("base", ["lg", "sm"])
    def test_merge_tasks_large_to_small(how, npartitions, base):
        size_lg = 3000
        size_sm = 300
        npartitions_lg = 30
        npartitions_sm = 3
        broadcast_bias = 1.0  # Prioritize broadcast
    
        lg = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_lg),
                "y": np.arange(size_lg),
            }
        )
        ddf_lg = dd.from_pandas(lg, npartitions=npartitions_lg)
    
        sm = pd.DataFrame(
            {
                "x": np.random.choice(np.arange(100), size_sm),
                "y": np.arange(size_sm),
            }
        )
        ddf_sm = dd.from_pandas(sm, npartitions=npartitions_sm)
    
        if base == "lg":
            left = lg
            ddf_left = ddf_lg
            right = sm
            ddf_right = ddf_sm
        else:
            left = sm
            ddf_left = ddf_sm
            right = lg
            ddf_right = ddf_lg
    
        dd_result = dd.merge(
            ddf_left,
            ddf_right,
            on="y",
            how=how,
            npartitions=npartitions,
            broadcast=broadcast_bias,
            shuffle_method="tasks",
        )
>       pd_result = pd.merge(left, right, on="y", how=how)

dask/dataframe/tests/test_multi.py:2372: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =        x_x    y  x_y
0     97.0  0.0   58
1     27.0  1.0   41
2     29.0  2.0   71
3     11.0  3.0   79
4     26.0  4...  NaN   53
2996   NaN  NaN   31
2997   NaN  NaN   52
2998   NaN  NaN   64
2999   NaN  NaN   41

[3000 rows x 3 columns]
key = 'y'
value = 0          0
1          1
2          2
3          3
4          4
        ... 
2995    2995
2996    2996
2997    2997
2998    2998
2999    2999
Length: 3000, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'first', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'first'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'first'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'first', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'first', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'first'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'first'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'first', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'first', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'first'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'first'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'first', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'first', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'first'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'first'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'last', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'last', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'last'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'last'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'last', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'last', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'last'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'last'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'last', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'last', groupby = 'cat_1'
observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'last'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'last'
groupby = ['cat_1', 'cat_2'], observed = True

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_get_dummies_categories_order _______________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_get_dummies_categories_order():
        df = pd.DataFrame({"a": [0.0, 0.0, 1.0, 1.0, 0.0], "b": [1.0, 0.0, 1.0, 0.0, 1.0]})
        ddf = dd.from_pandas(df, npartitions=1)
>       ddf = ddf.categorize(columns=["a", "b"])

dask/dataframe/tests/test_reshape.py:37: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3802: in categorize
    return new_collection(Categorize(self, categories, index))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_categorical.py:175: in _meta
    return _categorize_block(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/categorical.py:25: in _categorize_block
    df[col] = df[col].astype(cat_dtype)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = 'a'
value = Series([], Name: a, dtype: category
Categories (2, float64): [0.0, 1.0])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'last', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_get_dummies_errors ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_get_dummies_errors():
        with pytest.raises(NotImplementedError):
            # not Categorical
            s = pd.Series([1, 1, 1, 2, 2, 1, 3, 4])
            ds = dd.from_pandas(s, 2)
            dd.get_dummies(ds)
    
        # unknown categories
        df = pd.DataFrame({"x": list("abcbc"), "y": list("bcbcb")})
        ddf = dd.from_pandas(df, npartitions=2)
>       ddf = ddf.astype("category")

dask/dataframe/tests/test_reshape.py:178: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:1902: in astype
    return new_collection(self.expr.astype(dtypes))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1576: in _meta
    meta = clear_known_categories(meta)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:250: in clear_known_categories
    x[c] = x[c].cat.set_categories([UNKNOWN_CATEGORIES])
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = 'x'
value = Series([], Name: x, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'last', groupby = 'cat_1'
observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-ordered-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = True, agg_func = 'last'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_ test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-unordererd-unknown] _
[gw1] linux -- Python 3.14.0 /usr/bin/python3

known_cats = False, ordered_cats = False, agg_func = 'last'
groupby = ['cat_1', 'cat_2'], observed = False

    @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"])
    @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"])
    @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]])
    @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"])
    def test_groupby_aggregate_categorical_observed(
        known_cats, ordered_cats, agg_func, groupby, observed
    ):
        if agg_func in ["cov", "corr", "nunique"]:
            pytest.skip("Not implemented for DataFrameGroupBy yet.")
        if agg_func == "median" and isinstance(groupby, str):
            pytest.skip("Can't calculate median over categorical")
        if agg_func == "median":
            pytest.skip("Can't deal with unobserved cats in median at the moment")
        if agg_func in ["sum", "count", "prod"] and groupby != "cat_1":
            pytest.skip("Gives zeros rather than nans.")
        if agg_func in ["std", "var"] and observed:
            pytest.skip("Can't calculate observed with all nans")
        if agg_func in ["sum", "prod"]:
            pytest.xfail("Not implemented for category type with pandas 2.0")
    
        pdf = pd.DataFrame(
            {
                "cat_1": pd.Categorical(
                    list("AB"), categories=list("ABCDE"), ordered=ordered_cats
                ),
                "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats),
                "value_1": np.random.uniform(size=2),
            }
        )
        ddf = dd.from_pandas(pdf, 2)
    
        if not known_cats:
>           ddf["cat_1"] = ddf["cat_1"].cat.as_unknown()

dask/dataframe/tests/test_groupby.py:2832: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [cat_1, cat_2, value_1]
Index: [], key = 'cat_1'
value = Series([], Name: cat_1, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_pivot_table_errors ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_pivot_table_errors():
        df = pd.DataFrame(
            {
                "A": np.random.choice(list("abc"), size=10),
                "B": np.random.randn(10),
                "C": pd.Categorical(np.random.choice(list("abc"), size=10)),
            }
        )
        ddf = dd.from_pandas(df, 2)
    
        msg = "'index' must be the name of an existing column"
        with pytest.raises(ValueError) as err:
            dd.pivot_table(ddf, index=["A"], columns="C", values="B")
        assert msg in str(err.value)
        msg = "'columns' must be the name of an existing column"
        with pytest.raises(ValueError) as err:
            dd.pivot_table(ddf, index="A", columns=["C"], values="B")
        assert msg in str(err.value)
        msg = "'values' must refer to an existing column or columns"
        with pytest.raises(ValueError) as err:
            dd.pivot_table(ddf, index="A", columns="C", values=[["B"]])
        assert msg in str(err.value)
    
        msg = "aggfunc must be either 'mean', 'sum', 'count', 'first', 'last'"
        with pytest.raises(ValueError) as err:
            dd.pivot_table(ddf, index="A", columns="C", values="B", aggfunc=["sum"])
        assert msg in str(err.value)
    
        with pytest.raises(ValueError) as err:
            dd.pivot_table(ddf, index="A", columns="C", values="B", aggfunc="xx")
        assert msg in str(err.value)
    
        # unknown categories
>       ddf["C"] = ddf.C.cat.as_unknown()

dask/dataframe/tests/test_reshape.py:318: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:3051: in __setitem__
    out = self.assign(**{key: value})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2833: in assign
    result = new_collection(expr.Assign(result, *args))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_collections.py:8: in new_collection
    meta = expr._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B, C]
Index: [], key = 'C'
value = Series([], Name: C, dtype: category
Categories (1, object): ['__UNKNOWN_CATEGORIES__'])

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_groupby_numeric_only_true[disk-cumprod] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize(
        "func",
        [
            "min",
            "max",
            "sum",
            "prod",
            "first",
            "last",
            "corr",
            "cov",
            "cumprod",
            "cumsum",
            "mean",
            "median",
            "std",
            "var",
        ],
    )
    def test_groupby_numeric_only_true(func):
        df = pd.DataFrame({"A": [1, 1, 2, 2], "B": [3, 4, 3, 4], "C": ["a", "b", "c", "d"]})
        ddf = dd.from_pandas(df, npartitions=2)
        ddf_result = getattr(ddf.groupby("A"), func)(numeric_only=True)
        pdf_result = getattr(df.groupby("A"), func)(numeric_only=True)
>       assert_eq(ddf_result, pdf_result)

dask/dataframe/tests/test_groupby.py:3170: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [B]
Index: [], key = '_by_A'
value = Series([], Name: A, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_groupby_numeric_only_true[disk-cumsum] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize(
        "func",
        [
            "min",
            "max",
            "sum",
            "prod",
            "first",
            "last",
            "corr",
            "cov",
            "cumprod",
            "cumsum",
            "mean",
            "median",
            "std",
            "var",
        ],
    )
    def test_groupby_numeric_only_true(func):
        df = pd.DataFrame({"A": [1, 1, 2, 2], "B": [3, 4, 3, 4], "C": ["a", "b", "c", "d"]})
        ddf = dd.from_pandas(df, npartitions=2)
        ddf_result = getattr(ddf.groupby("A"), func)(numeric_only=True)
        pdf_result = getattr(df.groupby("A"), func)(numeric_only=True)
>       assert_eq(ddf_result, pdf_result)

dask/dataframe/tests/test_groupby.py:3170: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [B]
Index: [], key = '_by_A'
value = Series([], Name: A, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_groupby_numeric_only_true[tasks-cumprod] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumprod'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize(
        "func",
        [
            "min",
            "max",
            "sum",
            "prod",
            "first",
            "last",
            "corr",
            "cov",
            "cumprod",
            "cumsum",
            "mean",
            "median",
            "std",
            "var",
        ],
    )
    def test_groupby_numeric_only_true(func):
        df = pd.DataFrame({"A": [1, 1, 2, 2], "B": [3, 4, 3, 4], "C": ["a", "b", "c", "d"]})
        ddf = dd.from_pandas(df, npartitions=2)
        ddf_result = getattr(ddf.groupby("A"), func)(numeric_only=True)
        pdf_result = getattr(df.groupby("A"), func)(numeric_only=True)
>       assert_eq(ddf_result, pdf_result)

dask/dataframe/tests/test_groupby.py:3170: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [B]
Index: [], key = '_by_A'
value = Series([], Name: A, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_groupby_numeric_only_true[tasks-cumsum] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

func = 'cumsum'

    @pytest.mark.skipif(
        WINDOWS and sys.version_info < (3, 11),
        reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597",
    )
    @pytest.mark.parametrize(
        "func",
        [
            "min",
            "max",
            "sum",
            "prod",
            "first",
            "last",
            "corr",
            "cov",
            "cumprod",
            "cumsum",
            "mean",
            "median",
            "std",
            "var",
        ],
    )
    def test_groupby_numeric_only_true(func):
        df = pd.DataFrame({"A": [1, 1, 2, 2], "B": [3, 4, 3, 4], "C": ["a", "b", "c", "d"]})
        ddf = dd.from_pandas(df, npartitions=2)
        ddf_result = getattr(ddf.groupby("A"), func)(numeric_only=True)
        pdf_result = getattr(df.groupby("A"), func)(numeric_only=True)
>       assert_eq(ddf_result, pdf_result)

dask/dataframe/tests/test_groupby.py:3170: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:681: in compute
    results = schedule(expr, keys, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:547: in __dask_graph__
    layers.append(expr._layer())
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:271: in _layer
    (self._name, i): self._task((self._name, i), i)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:739: in _task
    "_meta": self._meta,
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:695: in _meta
    return _get_meta_map_partitions(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:4078: in _get_meta_map_partitions
    meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dispatch.py:121: in make_meta
    if not isinstance(x, (pd.Series, pd.DataFrame, pd.Index)) and hasattr(x, "_meta"):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [B]
Index: [], key = '_by_A'
value = Series([], Name: A, dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_sort_values_tasks_backend[False-x-pandas] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

backend = 'pandas', by = 'x', ascending = False

    @pytest.mark.parametrize(
        "backend", ["pandas", pytest.param("cudf", marks=pytest.mark.gpu)]
    )
    @pytest.mark.parametrize("by", ["x", "z", ["x", "z"], ["z", "x"]])
    @pytest.mark.parametrize("ascending", [True, False])
    def test_sort_values_tasks_backend(backend, by, ascending):
        if backend == "cudf":
            pytest.importorskip("dask_cudf")
        pdf = pd.DataFrame(
            {"x": range(10), "y": [1, 2, 3, 4, 5] * 2, "z": ["cat", "dog"] * 5}
        )
        ddf = dd.from_pandas(pdf, npartitions=10)
        if backend == "cudf":
            ddf = ddf.to_backend(backend)
    
        expect = pdf.sort_values(by=by, ascending=ascending)
        got = dd.DataFrame.sort_values(
            ddf, by=by, ascending=ascending, shuffle_method="tasks"
        )
>       dd.assert_eq(got, expect, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1106: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:374: in _meta
    meta = self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_sort_values_tasks_backend[False-z-pandas] ________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

backend = 'pandas', by = 'z', ascending = False

    @pytest.mark.parametrize(
        "backend", ["pandas", pytest.param("cudf", marks=pytest.mark.gpu)]
    )
    @pytest.mark.parametrize("by", ["x", "z", ["x", "z"], ["z", "x"]])
    @pytest.mark.parametrize("ascending", [True, False])
    def test_sort_values_tasks_backend(backend, by, ascending):
        if backend == "cudf":
            pytest.importorskip("dask_cudf")
        pdf = pd.DataFrame(
            {"x": range(10), "y": [1, 2, 3, 4, 5] * 2, "z": ["cat", "dog"] * 5}
        )
        ddf = dd.from_pandas(pdf, npartitions=10)
        if backend == "cudf":
            ddf = ddf.to_backend(backend)
    
        expect = pdf.sort_values(by=by, ascending=ascending)
        got = dd.DataFrame.sort_values(
            ddf, by=by, ascending=ascending, shuffle_method="tasks"
        )
>       dd.assert_eq(got, expect, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1106: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:374: in _meta
    meta = self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_sort_values_tasks_backend[False-by2-pandas] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

backend = 'pandas', by = ['x', 'z'], ascending = False

    @pytest.mark.parametrize(
        "backend", ["pandas", pytest.param("cudf", marks=pytest.mark.gpu)]
    )
    @pytest.mark.parametrize("by", ["x", "z", ["x", "z"], ["z", "x"]])
    @pytest.mark.parametrize("ascending", [True, False])
    def test_sort_values_tasks_backend(backend, by, ascending):
        if backend == "cudf":
            pytest.importorskip("dask_cudf")
        pdf = pd.DataFrame(
            {"x": range(10), "y": [1, 2, 3, 4, 5] * 2, "z": ["cat", "dog"] * 5}
        )
        ddf = dd.from_pandas(pdf, npartitions=10)
        if backend == "cudf":
            ddf = ddf.to_backend(backend)
    
        expect = pdf.sort_values(by=by, ascending=ascending)
        got = dd.DataFrame.sort_values(
            ddf, by=by, ascending=ascending, shuffle_method="tasks"
        )
>       dd.assert_eq(got, expect, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1106: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:374: in _meta
    meta = self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_sort_values_tasks_backend[False-by3-pandas] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

backend = 'pandas', by = ['z', 'x'], ascending = False

    @pytest.mark.parametrize(
        "backend", ["pandas", pytest.param("cudf", marks=pytest.mark.gpu)]
    )
    @pytest.mark.parametrize("by", ["x", "z", ["x", "z"], ["z", "x"]])
    @pytest.mark.parametrize("ascending", [True, False])
    def test_sort_values_tasks_backend(backend, by, ascending):
        if backend == "cudf":
            pytest.importorskip("dask_cudf")
        pdf = pd.DataFrame(
            {"x": range(10), "y": [1, 2, 3, 4, 5] * 2, "z": ["cat", "dog"] * 5}
        )
        ddf = dd.from_pandas(pdf, npartitions=10)
        if backend == "cudf":
            ddf = ddf.to_backend(backend)
    
        expect = pdf.sort_values(by=by, ascending=ascending)
        got = dd.DataFrame.sort_values(
            ddf, by=by, ascending=ascending, shuffle_method="tasks"
        )
>       dd.assert_eq(got, expect, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1106: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:374: in _meta
    meta = self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data0-5-a-True-first] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = 'a', ascending = True, na_position = 'first'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_sort_values_with_nulls[data0-5-a-True-last] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = 'a', ascending = True, na_position = 'last'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data0-5-a-False-first] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = 'a', ascending = False, na_position = 'first'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data0-5-a-False-last] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = 'a', ascending = False, na_position = 'last'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data0-5-b-True-first] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = 'b', ascending = True, na_position = 'first'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_sort_values_with_nulls[data0-5-b-True-last] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = 'b', ascending = True, na_position = 'last'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data0-5-b-False-first] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = 'b', ascending = False, na_position = 'first'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data0-5-b-False-last] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = 'b', ascending = False, na_position = 'last'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_sort_values_with_nulls[data0-5-by2-True-first] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = ['a', 'b'], ascending = True, na_position = 'first'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data0-5-by2-True-last] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = ['a', 'b'], ascending = True, na_position = 'last'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_sort_values_with_nulls[data0-5-by2-False-first] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = ['a', 'b'], ascending = False, na_position = 'first'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_sort_values_with_nulls[data0-5-by2-False-last] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [None, None, None, None, None, None, ...]}
nparts = 5, by = ['a', 'b'], ascending = False, na_position = 'last'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_set_index_general[disk-4] ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 4, shuffle_method = 'disk'

    @pytest.mark.parametrize(
        "npartitions", [1, 4, 7, pytest.param(23, marks=pytest.mark.slow)]
    )
    def test_set_index_general(npartitions, shuffle_method):
        names = ["alice", "bob", "ricky"]
        df = pd.DataFrame(
            {
                "x": np.random.random(100),
                "y": np.random.random(100) // 0.2,
                "z": np.random.choice(names, 100),
            },
            index=np.random.random(100),
        )
        # Ensure extension dtypes work
        df = df.astype({"x": "Float64", "z": "string"})
    
        ddf = dd.from_pandas(df, npartitions=npartitions)
    
>       assert_eq(df.set_index("x"), ddf.set_index("x", shuffle_method=shuffle_method))

dask/dataframe/tests/test_shuffle.py:183: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data1-5-a-True-first] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [19, 18, 17, 16, 15, 14, ...]}
nparts = 5, by = 'a', ascending = True, na_position = 'first'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_set_index_general[disk-7] ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 7, shuffle_method = 'disk'

    @pytest.mark.parametrize(
        "npartitions", [1, 4, 7, pytest.param(23, marks=pytest.mark.slow)]
    )
    def test_set_index_general(npartitions, shuffle_method):
        names = ["alice", "bob", "ricky"]
        df = pd.DataFrame(
            {
                "x": np.random.random(100),
                "y": np.random.random(100) // 0.2,
                "z": np.random.choice(names, 100),
            },
            index=np.random.random(100),
        )
        # Ensure extension dtypes work
        df = df.astype({"x": "Float64", "z": "string"})
    
        ddf = dd.from_pandas(df, npartitions=npartitions)
    
>       assert_eq(df.set_index("x"), ddf.set_index("x", shuffle_method=shuffle_method))

dask/dataframe/tests/test_shuffle.py:183: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_sort_values_with_nulls[data1-5-a-True-last] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [19, 18, 17, 16, 15, 14, ...]}
nparts = 5, by = 'a', ascending = True, na_position = 'last'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_set_index_general[tasks-4] ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 4, shuffle_method = 'tasks'

    @pytest.mark.parametrize(
        "npartitions", [1, 4, 7, pytest.param(23, marks=pytest.mark.slow)]
    )
    def test_set_index_general(npartitions, shuffle_method):
        names = ["alice", "bob", "ricky"]
        df = pd.DataFrame(
            {
                "x": np.random.random(100),
                "y": np.random.random(100) // 0.2,
                "z": np.random.choice(names, 100),
            },
            index=np.random.random(100),
        )
        # Ensure extension dtypes work
        df = df.astype({"x": "Float64", "z": "string"})
    
        ddf = dd.from_pandas(df, npartitions=npartitions)
    
>       assert_eq(df.set_index("x"), ddf.set_index("x", shuffle_method=shuffle_method))

dask/dataframe/tests/test_shuffle.py:183: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data1-5-a-False-first] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [19, 18, 17, 16, 15, 14, ...]}
nparts = 5, by = 'a', ascending = False, na_position = 'first'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_set_index_general[tasks-7] ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 7, shuffle_method = 'tasks'

    @pytest.mark.parametrize(
        "npartitions", [1, 4, 7, pytest.param(23, marks=pytest.mark.slow)]
    )
    def test_set_index_general(npartitions, shuffle_method):
        names = ["alice", "bob", "ricky"]
        df = pd.DataFrame(
            {
                "x": np.random.random(100),
                "y": np.random.random(100) // 0.2,
                "z": np.random.choice(names, 100),
            },
            index=np.random.random(100),
        )
        # Ensure extension dtypes work
        df = df.astype({"x": "Float64", "z": "string"})
    
        ddf = dd.from_pandas(df, npartitions=npartitions)
    
>       assert_eq(df.set_index("x"), ddf.set_index("x", shuffle_method=shuffle_method))

dask/dataframe/tests/test_shuffle.py:183: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data1-5-a-False-last] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [19, 18, 17, 16, 15, 14, ...]}
nparts = 5, by = 'a', ascending = False, na_position = 'last'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_set_index_string[disk-string[python]] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'disk', string_dtype = 'string[python]'

    @pytest.mark.parametrize(
        "string_dtype", ["string[python]", "string[pyarrow]", "object"]
    )
    def test_set_index_string(shuffle_method, string_dtype):
        if string_dtype == "string[pyarrow]":
            pytest.importorskip("pyarrow")
        names = ["alice", "bob", "ricky"]
        df = pd.DataFrame(
            {
                "x": np.random.random(100),
                "y": np.random.choice(names, 100),
            },
            index=np.random.random(100),
        )
        df = df.astype({"y": string_dtype})
        ddf = dd.from_pandas(df, npartitions=10)
>       assert_eq(df.set_index("y"), ddf.set_index("y", shuffle_method=shuffle_method))

dask/dataframe/tests/test_shuffle.py:212: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data1-5-b-True-first] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [19, 18, 17, 16, 15, 14, ...]}
nparts = 5, by = 'b', ascending = True, na_position = 'first'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_set_index_string[disk-string[pyarrow]] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'disk', string_dtype = 'string[pyarrow]'

    @pytest.mark.parametrize(
        "string_dtype", ["string[python]", "string[pyarrow]", "object"]
    )
    def test_set_index_string(shuffle_method, string_dtype):
        if string_dtype == "string[pyarrow]":
            pytest.importorskip("pyarrow")
        names = ["alice", "bob", "ricky"]
        df = pd.DataFrame(
            {
                "x": np.random.random(100),
                "y": np.random.choice(names, 100),
            },
            index=np.random.random(100),
        )
        df = df.astype({"y": string_dtype})
        ddf = dd.from_pandas(df, npartitions=10)
>       assert_eq(df.set_index("y"), ddf.set_index("y", shuffle_method=shuffle_method))

dask/dataframe/tests/test_shuffle.py:212: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_set_index_string[disk-object] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'disk', string_dtype = 'object'

    @pytest.mark.parametrize(
        "string_dtype", ["string[python]", "string[pyarrow]", "object"]
    )
    def test_set_index_string(shuffle_method, string_dtype):
        if string_dtype == "string[pyarrow]":
            pytest.importorskip("pyarrow")
        names = ["alice", "bob", "ricky"]
        df = pd.DataFrame(
            {
                "x": np.random.random(100),
                "y": np.random.choice(names, 100),
            },
            index=np.random.random(100),
        )
        df = df.astype({"y": string_dtype})
        ddf = dd.from_pandas(df, npartitions=10)
>       assert_eq(df.set_index("y"), ddf.set_index("y", shuffle_method=shuffle_method))

dask/dataframe/tests/test_shuffle.py:212: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_sort_values_with_nulls[data1-5-b-True-last] _______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [19, 18, 17, 16, 15, 14, ...]}
nparts = 5, by = 'b', ascending = True, na_position = 'last'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_set_index_string[tasks-string[python]] __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks', string_dtype = 'string[python]'

    @pytest.mark.parametrize(
        "string_dtype", ["string[python]", "string[pyarrow]", "object"]
    )
    def test_set_index_string(shuffle_method, string_dtype):
        if string_dtype == "string[pyarrow]":
            pytest.importorskip("pyarrow")
        names = ["alice", "bob", "ricky"]
        df = pd.DataFrame(
            {
                "x": np.random.random(100),
                "y": np.random.choice(names, 100),
            },
            index=np.random.random(100),
        )
        df = df.astype({"y": string_dtype})
        ddf = dd.from_pandas(df, npartitions=10)
>       assert_eq(df.set_index("y"), ddf.set_index("y", shuffle_method=shuffle_method))

dask/dataframe/tests/test_shuffle.py:212: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_sort_values_with_nulls[data1-5-by2-True-first] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [19, 18, 17, 16, 15, 14, ...]}
nparts = 5, by = ['a', 'b'], ascending = True, na_position = 'first'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_set_index_string[tasks-string[pyarrow]] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks', string_dtype = 'string[pyarrow]'

    @pytest.mark.parametrize(
        "string_dtype", ["string[python]", "string[pyarrow]", "object"]
    )
    def test_set_index_string(shuffle_method, string_dtype):
        if string_dtype == "string[pyarrow]":
            pytest.importorskip("pyarrow")
        names = ["alice", "bob", "ricky"]
        df = pd.DataFrame(
            {
                "x": np.random.random(100),
                "y": np.random.choice(names, 100),
            },
            index=np.random.random(100),
        )
        df = df.astype({"y": string_dtype})
        ddf = dd.from_pandas(df, npartitions=10)
>       assert_eq(df.set_index("y"), ddf.set_index("y", shuffle_method=shuffle_method))

dask/dataframe/tests/test_shuffle.py:212: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________ test_sort_values_with_nulls[data1-5-by2-True-last] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [19, 18, 17, 16, 15, 14, ...]}
nparts = 5, by = ['a', 'b'], ascending = True, na_position = 'last'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_set_index_string[tasks-object] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks', string_dtype = 'object'

    @pytest.mark.parametrize(
        "string_dtype", ["string[python]", "string[pyarrow]", "object"]
    )
    def test_set_index_string(shuffle_method, string_dtype):
        if string_dtype == "string[pyarrow]":
            pytest.importorskip("pyarrow")
        names = ["alice", "bob", "ricky"]
        df = pd.DataFrame(
            {
                "x": np.random.random(100),
                "y": np.random.choice(names, 100),
            },
            index=np.random.random(100),
        )
        df = df.astype({"y": string_dtype})
        ddf = dd.from_pandas(df, npartitions=10)
>       assert_eq(df.set_index("y"), ddf.set_index("y", shuffle_method=shuffle_method))

dask/dataframe/tests/test_shuffle.py:212: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_sort_values_with_nulls[data1-5-by2-False-first] _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [19, 18, 17, 16, 15, 14, ...]}
nparts = 5, by = ['a', 'b'], ascending = False, na_position = 'first'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_set_index_names[tasks] __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks'

    def test_set_index_names(shuffle_method):
        if shuffle_method == "disk":
            pytest.xfail("dsk names in disk shuffle are not deterministic")
    
        df = pd.DataFrame(
            {"x": np.random.random(100), "y": np.random.random(100) // 0.2},
            index=np.random.random(100),
        )
    
        ddf = dd.from_pandas(df, npartitions=4)
    
>       assert set(ddf.set_index("x", shuffle_method=shuffle_method).dask) == set(
            ddf.set_index("x", shuffle_method=shuffle_method).dask
        )

dask/dataframe/tests/test_shuffle.py:239: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:536: in dask
    return self.__dask_graph__()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:540: in __dask_graph__
    out = out.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_sort_values_with_nulls[data1-5-by2-False-last] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = {'a': [0, 1, 2, 3, 4, 5, ...], 'b': [19, 18, 17, 16, 15, 14, ...]}
nparts = 5, by = ['a', 'b'], ascending = False, na_position = 'last'

    @pytest.mark.parametrize("na_position", ["first", "last"])
    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nparts", [1, 5])
    @pytest.mark.parametrize(
        "data",
        [
            {
                "a": list(range(50)) + [None] * 50 + list(range(50, 100)),
                "b": [None] * 100 + list(range(100, 150)),
            },
            {
                "a": list(range(15)) + [None] * 5,
                "b": list(reversed(range(20))),
            },
        ],
    )
    def test_sort_values_with_nulls(data, nparts, by, ascending, na_position):
        df = pd.DataFrame(data)
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending, na_position=na_position)
        expect = df.sort_values(by=by, ascending=ascending, na_position=na_position)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1151: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_set_index_2[disk] ____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'disk'

    def test_set_index_2(shuffle_method):
        df = dd.demo.make_timeseries(
            "2000",
            "2004",
            {"value": float, "name": str, "id": int},
            freq="2h",
            partition_freq=f"1{ME}",
            seed=1,
        )
    
        df2 = df.set_index("name", shuffle_method=shuffle_method)
>       df2.value.sum().compute(scheduler="sync")

dask/dataframe/tests/test_shuffle.py:267: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [value, name]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_shuffle_by_as_list ____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_shuffle_by_as_list():
        df = pd.DataFrame({"a": [1, 3, 2]})
        ddf = dd.from_pandas(df, npartitions=3)
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=["a"], ascending=True)
        expect = pd.DataFrame({"a": [1, 2, 3]})
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1169: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_set_index_2[tasks] ____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks'

    def test_set_index_2(shuffle_method):
        df = dd.demo.make_timeseries(
            "2000",
            "2004",
            {"value": float, "name": str, "id": int},
            freq="2h",
            partition_freq=f"1{ME}",
            seed=1,
        )
    
        df2 = df.set_index("name", shuffle_method=shuffle_method)
>       df2.value.sum().compute(scheduler="sync")

dask/dataframe/tests/test_shuffle.py:267: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [value, name]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_sort_values_custom_function[10-by0] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

by = ['a', 'b'], nparts = 10

    @pytest.mark.parametrize("by", [["a", "b"], ["b", "a"]])
    @pytest.mark.parametrize("nparts", [1, 10])
    def test_sort_values_custom_function(by, nparts):
        df = pd.DataFrame({"a": [1, 2, 3] * 20, "b": [4, 5, 6, 7] * 15})
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        def f(partition, by_columns, ascending, na_position, **kwargs):
            return partition.sort_values(
                by_columns, ascending=ascending, na_position=na_position
            )
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(
                by=by[0], sort_function=f, sort_function_kwargs={"by_columns": by}
            )
        expect = df.sort_values(by=by)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1189: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_set_index_3[disk] ____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'disk'

    def test_set_index_3(shuffle_method):
        df = pd.DataFrame(np.random.random((10, 2)), columns=["x", "y"])
        ddf = dd.from_pandas(df, npartitions=5)
    
        ddf2 = ddf.set_index(
            "x", shuffle_method=shuffle_method, max_branch=2, npartitions=ddf.npartitions
        )
        df2 = df.set_index("x")
>       assert_eq(df2, ddf2)

dask/dataframe/tests/test_shuffle.py:278: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________ test_sort_values_custom_function[10-by1] ___________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

by = ['b', 'a'], nparts = 10

    @pytest.mark.parametrize("by", [["a", "b"], ["b", "a"]])
    @pytest.mark.parametrize("nparts", [1, 10])
    def test_sort_values_custom_function(by, nparts):
        df = pd.DataFrame({"a": [1, 2, 3] * 20, "b": [4, 5, 6, 7] * 15})
        ddf = dd.from_pandas(df, npartitions=nparts)
    
        def f(partition, by_columns, ascending, na_position, **kwargs):
            return partition.sort_values(
                by_columns, ascending=ascending, na_position=na_position
            )
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(
                by=by[0], sort_function=f, sort_function_kwargs={"by_columns": by}
            )
        expect = df.sort_values(by=by)
>       dd.assert_eq(got, expect, check_index=False)

dask/dataframe/tests/test_shuffle.py:1189: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_set_index_3[tasks] ____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks'

    def test_set_index_3(shuffle_method):
        df = pd.DataFrame(np.random.random((10, 2)), columns=["x", "y"])
        ddf = dd.from_pandas(df, npartitions=5)
    
        ddf2 = ddf.set_index(
            "x", shuffle_method=shuffle_method, max_branch=2, npartitions=ddf.npartitions
        )
        df2 = df.set_index("x")
>       assert_eq(df2, ddf2)

dask/dataframe/tests/test_shuffle.py:278: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_set_index_no_sort[True-True] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

drop = True, append = True

    @pytest.mark.parametrize("drop", (True, False))
    @pytest.mark.parametrize("append", (True, False))
    def test_set_index_no_sort(drop, append):
        """
        GH10333 - Allow setting index on existing partitions without
        computing new divisions and repartitioning.
        """
        df = pd.DataFrame({"col1": [2, 4, 1, 3, 5], "col2": [1, 2, 3, 4, 5]})
        ddf = dd.from_pandas(df, npartitions=2)
    
        assert ddf.npartitions > 1
    
        # Default is sort=True
        # Index in ddf will be same values, but sorted
        df_result = df.set_index("col1")
        ddf_result = ddf.set_index("col1")
        assert ddf_result.known_divisions
>       assert_eq(ddf_result, df_result.sort_index(), sort_results=False)

dask/dataframe/tests/test_shuffle.py:299: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [col1, col2]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________________ test_sort_values_bool_ascending ________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_sort_values_bool_ascending():
        df = pd.DataFrame({"a": [1, 2, 3] * 20, "b": [4, 5, 6, 7] * 15})
        ddf = dd.from_pandas(df, npartitions=10)
    
        # attempt to sort with list of ascending booleans
        with pytest.raises(ValueError, match="length"):
            ddf.sort_values(by="a", ascending=[True, False])
        with pytest.raises(ValueError, match="length"):
            ddf.sort_values(by=["a", "b"], ascending=[True])
>       assert_eq(
            ddf.sort_values(by="a", ascending=[True]),
            df.sort_values(by="a", ascending=[True]),
        )

dask/dataframe/tests/test_shuffle.py:1201: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_set_index_no_sort[True-False] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

drop = False, append = True

    @pytest.mark.parametrize("drop", (True, False))
    @pytest.mark.parametrize("append", (True, False))
    def test_set_index_no_sort(drop, append):
        """
        GH10333 - Allow setting index on existing partitions without
        computing new divisions and repartitioning.
        """
        df = pd.DataFrame({"col1": [2, 4, 1, 3, 5], "col2": [1, 2, 3, 4, 5]})
        ddf = dd.from_pandas(df, npartitions=2)
    
        assert ddf.npartitions > 1
    
        # Default is sort=True
        # Index in ddf will be same values, but sorted
        df_result = df.set_index("col1")
        ddf_result = ddf.set_index("col1")
        assert ddf_result.known_divisions
>       assert_eq(ddf_result, df_result.sort_index(), sort_results=False)

dask/dataframe/tests/test_shuffle.py:299: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [col1, col2]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_sort_values_timestamp[3] _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

npartitions = 3

    @pytest.mark.parametrize("npartitions", [1, 3])
    def test_sort_values_timestamp(npartitions):
        # Regression test for https://github.com/dask/dask/issues/9641
        df = pd.DataFrame.from_records(
            [
                [pd.Timestamp("2002-01-11 21:00:01+0000", tz="UTC"), 4223, 54719.0],
                [pd.Timestamp("2002-01-14 21:00:01+0000", tz="UTC"), 6942, 19223.0],
                [pd.Timestamp("2002-01-15 21:00:01+0000", tz="UTC"), 12551, 72865.0],
                [pd.Timestamp("2002-01-23 21:00:01+0000", tz="UTC"), 6005, 57670.0],
                [pd.Timestamp("2002-01-29 21:00:01+0000", tz="UTC"), 2043, 58600.0],
                [pd.Timestamp("2002-02-01 21:00:01+0000", tz="UTC"), 6909, 8459.0],
                [pd.Timestamp("2002-01-14 21:00:01+0000", tz="UTC"), 5326, 77339.0],
                [pd.Timestamp("2002-01-14 21:00:01+0000", tz="UTC"), 4711, 54135.0],
                [pd.Timestamp("2002-01-22 21:00:01+0000", tz="UTC"), 103, 57627.0],
                [pd.Timestamp("2002-01-30 21:00:01+0000", tz="UTC"), 16862, 54458.0],
                [pd.Timestamp("2002-01-31 21:00:01+0000", tz="UTC"), 4143, 56280.0],
            ],
            columns=["time", "id1", "id2"],
        )
        ddf = dd.from_pandas(df, npartitions=npartitions)
        result = ddf.sort_values("time")
        expected = df.sort_values("time")
>       assert_eq(result, expected)

dask/dataframe/tests/test_shuffle.py:1233: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [time, id1, id2]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_set_index_no_sort[False-True] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

drop = True, append = False

    @pytest.mark.parametrize("drop", (True, False))
    @pytest.mark.parametrize("append", (True, False))
    def test_set_index_no_sort(drop, append):
        """
        GH10333 - Allow setting index on existing partitions without
        computing new divisions and repartitioning.
        """
        df = pd.DataFrame({"col1": [2, 4, 1, 3, 5], "col2": [1, 2, 3, 4, 5]})
        ddf = dd.from_pandas(df, npartitions=2)
    
        assert ddf.npartitions > 1
    
        # Default is sort=True
        # Index in ddf will be same values, but sorted
        df_result = df.set_index("col1")
        ddf_result = ddf.set_index("col1")
        assert ddf_result.known_divisions
>       assert_eq(ddf_result, df_result.sort_index(), sort_results=False)

dask/dataframe/tests/test_shuffle.py:299: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [col1, col2]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_set_index_pyarrow_dtype[data5-date32[pyarrow]] ______________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = [datetime.date(2022, 1, 1), datetime.date(1999, 12, 31)]
dtype = 'date32[pyarrow]'

    @pytest.mark.skipif(pa is None, reason="Need pyarrow")
    @pytest.mark.parametrize(
        "data, dtype",
        [
            (["a", "b"], "string[pyarrow]"),
            ([b"a", b"b"], "binary[pyarrow]"),
            # Should probably fix upstream, https://github.com/pandas-dev/pandas/issues/52590
            # (["a", "b"], pa.large_string()),
            # ([b"a", b"b"], pa.large_binary()),
            ([1, 2], "int64[pyarrow]"),
            ([1, 2], "float64[pyarrow]"),
            ([1, 2], "uint64[pyarrow]"),
            ([date(2022, 1, 1), date(1999, 12, 31)], "date32[pyarrow]"),
            (
                [pd.Timestamp("2022-01-01"), pd.Timestamp("2023-01-02")],
                "timestamp[ns][pyarrow]",
            ),
            ([Decimal("5"), Decimal("6.24")], "decimal128"),
            ([pd.Timedelta("1 day"), pd.Timedelta("20 days")], "duration[ns][pyarrow]"),
            ([time(12, 0), time(0, 12)], "time64[ns][pyarrow]"),
        ],
    )
    def test_set_index_pyarrow_dtype(data, dtype):
        if dtype == "decimal128":
            dtype = pd.ArrowDtype(pa.decimal128(10, 2))
        pdf = pd.DataFrame({"a": 1, "arrow_col": pd.Series(data, dtype=dtype)})
        ddf = dd.from_pandas(pdf, npartitions=2)
        pdf_result = pdf.set_index("arrow_col")
        ddf_result = ddf.set_index("arrow_col")
>       assert_eq(ddf_result, pdf_result)

dask/dataframe/tests/test_shuffle.py:1265: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, arrow_col]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________ test_set_index_no_sort[False-False] ______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

drop = False, append = False

    @pytest.mark.parametrize("drop", (True, False))
    @pytest.mark.parametrize("append", (True, False))
    def test_set_index_no_sort(drop, append):
        """
        GH10333 - Allow setting index on existing partitions without
        computing new divisions and repartitioning.
        """
        df = pd.DataFrame({"col1": [2, 4, 1, 3, 5], "col2": [1, 2, 3, 4, 5]})
        ddf = dd.from_pandas(df, npartitions=2)
    
        assert ddf.npartitions > 1
    
        # Default is sort=True
        # Index in ddf will be same values, but sorted
        df_result = df.set_index("col1")
        ddf_result = ddf.set_index("col1")
        assert ddf_result.known_divisions
>       assert_eq(ddf_result, df_result.sort_index(), sort_results=False)

dask/dataframe/tests/test_shuffle.py:299: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [col1, col2]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________ test_set_index_pyarrow_dtype[data9-time64[ns][pyarrow]] ____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

data = [datetime.time(12, 0), datetime.time(0, 12)]
dtype = 'time64[ns][pyarrow]'

    @pytest.mark.skipif(pa is None, reason="Need pyarrow")
    @pytest.mark.parametrize(
        "data, dtype",
        [
            (["a", "b"], "string[pyarrow]"),
            ([b"a", b"b"], "binary[pyarrow]"),
            # Should probably fix upstream, https://github.com/pandas-dev/pandas/issues/52590
            # (["a", "b"], pa.large_string()),
            # ([b"a", b"b"], pa.large_binary()),
            ([1, 2], "int64[pyarrow]"),
            ([1, 2], "float64[pyarrow]"),
            ([1, 2], "uint64[pyarrow]"),
            ([date(2022, 1, 1), date(1999, 12, 31)], "date32[pyarrow]"),
            (
                [pd.Timestamp("2022-01-01"), pd.Timestamp("2023-01-02")],
                "timestamp[ns][pyarrow]",
            ),
            ([Decimal("5"), Decimal("6.24")], "decimal128"),
            ([pd.Timedelta("1 day"), pd.Timedelta("20 days")], "duration[ns][pyarrow]"),
            ([time(12, 0), time(0, 12)], "time64[ns][pyarrow]"),
        ],
    )
    def test_set_index_pyarrow_dtype(data, dtype):
        if dtype == "decimal128":
            dtype = pd.ArrowDtype(pa.decimal128(10, 2))
        pdf = pd.DataFrame({"a": 1, "arrow_col": pd.Series(data, dtype=dtype)})
        ddf = dd.from_pandas(pdf, npartitions=2)
        pdf_result = pdf.set_index("arrow_col")
        ddf_result = ddf.set_index("arrow_col")
>       assert_eq(ddf_result, pdf_result)

dask/dataframe/tests/test_shuffle.py:1265: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, arrow_col]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_shuffle_sort[disk] ____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'disk'

    def test_shuffle_sort(shuffle_method):
        df = pd.DataFrame({"x": [1, 2, 3, 2, 1], "y": [9, 8, 7, 1, 5]})
        ddf = dd.from_pandas(df, npartitions=3)
    
        df2 = df.set_index("x").sort_index()
        ddf2 = ddf.set_index("x", shuffle_method=shuffle_method)
    
>       assert_eq(ddf2, df2, sort_results=False)

dask/dataframe/tests/test_shuffle.py:317: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_shuffle_nulls_introduced _________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_shuffle_nulls_introduced():
        df1 = pd.DataFrame([[True], [False]] * 50, columns=["A"])
        df1["B"] = list(range(100))
    
        df2 = pd.DataFrame(
            [[2, 3], [109, 2], [345, 3], [50, 7], [95, 1]], columns=["B", "C"]
        )
    
        ddf1 = dd.from_pandas(df1, npartitions=10)
        ddf2 = dd.from_pandas(df2, npartitions=1)
        meta = pd.Series(dtype=int, index=pd.Index([], dtype=bool, name="A"), name="A")
        include_groups = {"include_groups": False} if PANDAS_GE_220 else {}
        result = (
            dd.merge(ddf1, ddf2, how="outer", on="B")
            .groupby("A")
            .apply(lambda df: len(df), meta=meta, **include_groups)
        )
        expected = (
>           pd.merge(df1, df2, how="outer", on="B")
            .groupby("A")
            .apply(lambda df: len(df), **include_groups)
        )

dask/dataframe/tests/test_shuffle.py:1286: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:184: in merge
    return op.get_result(copy=copy)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:896: in get_result
    self._maybe_add_join_keys(result, left_indexer, right_indexer)
/usr/lib64/python3.14/site-packages/pandas/core/reshape/merge.py:1100: in _maybe_add_join_keys
    result[name] = result._constructor_sliced(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =          A     B    C
0     True   0.0  NaN
1    False   1.0  NaN
2     True   2.0  3.0
3    False   3.0  NaN
4     Tr....0  NaN
98    True  98.0  NaN
99   False  99.0  NaN
100    NaN   NaN  2.0
101    NaN   NaN  3.0

[102 rows x 3 columns]
key = 'B'
value = 0        0
1        1
2        2
3        3
4        4
      ... 
97      97
98      98
99      99
100    109
101    345
Length: 102, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_shuffle_sort[tasks] ___________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

shuffle_method = 'tasks'

    def test_shuffle_sort(shuffle_method):
        df = pd.DataFrame({"x": [1, 2, 3, 2, 1], "y": [9, 8, 7, 1, 5]})
        ddf = dd.from_pandas(df, npartitions=3)
    
        df2 = df.set_index("x").sort_index()
        ddf2 = ddf.set_index("x", shuffle_method=shuffle_method)
    
>       assert_eq(ddf2, df2, sort_results=False)

dask/dataframe/tests/test_shuffle.py:317: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_set_index_with_explicit_divisions ____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_set_index_with_explicit_divisions():
        df = pd.DataFrame({"x": [4, 1, 2, 5]}, index=[10, 20, 30, 40])
    
        ddf = dd.from_pandas(df, npartitions=2)
    
        def throw(*args, **kwargs):
            raise Exception()
    
        with dask.config.set(scheduler=throw):
            ddf2 = ddf.set_index("x", divisions=[1, 3, 5])
        assert ddf2.divisions == (1, 3, 5)
    
        df2 = df.set_index("x")
>       assert_eq(ddf2, df2)

dask/dataframe/tests/test_shuffle.py:356: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_set_index_divisions_2 __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_set_index_divisions_2():
        df = pd.DataFrame({"x": [1, 2, 3, 4, 5, 6], "y": list("abdabd")})
        ddf = dd.from_pandas(df, 2)
    
        result = ddf.set_index("y", divisions=["a", "c", "d"])
        assert result.divisions == ("a", "c", "d")
    
>       assert list(result.compute(scheduler="sync").index[-2:]) == ["d", "d"]

dask/dataframe/tests/test_shuffle.py:380: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_set_index[pandas] ____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

engine = 'pandas'

    @pytest.mark.parametrize(
        "engine", ["pandas", pytest.param("cudf", marks=pytest.mark.gpu)]
    )
    def test_set_index(engine):
        if engine == "cudf":
            # NOTE: engine == "cudf" requires cudf/dask_cudf,
            # will be skipped by non-GPU CI.
    
            pytest.importorskip("dask_cudf")
    
        dsk = {
            ("x", 0): pd.DataFrame({"a": [1, 2, 3], "b": [4, 2, 6]}, index=[0, 1, 3]),
            ("x", 1): pd.DataFrame({"a": [4, 5, 6], "b": [3, 5, 8]}, index=[5, 6, 8]),
            ("x", 2): pd.DataFrame({"a": [7, 8, 9], "b": [9, 1, 8]}, index=[9, 9, 9]),
        }
    
        d = dd.repartition(pd.concat(dsk.values()), [0, 4, 9, 9])
        full = d.compute()
    
        d2 = d.set_index("b", npartitions=3)
        assert d2.npartitions == 3
        assert d2.index.name == "b"
>       assert_eq(d2, full.set_index("b"))

dask/dataframe/tests/test_shuffle.py:515: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_set_index_datetime_precision[ns] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

unit = 'ns'

    @pytest.mark.parametrize("unit", ["ns", "us"])
    def test_set_index_datetime_precision(unit):
        # https://github.com/dask/dask/issues/6864
    
        df = pd.DataFrame(
            [
                [1567703791155681, 1],
                [1567703792155681, 2],
                [1567703790155681, 0],
                [1567703793155681, 3],
            ],
            columns=["ts", "rank"],
        )
        df.ts = pd.to_datetime(df.ts, unit=unit)
        ddf = dd.from_pandas(df, npartitions=2)
        ddf = ddf.set_index("ts")
    
>       assert_eq(ddf, df.set_index("ts"))

dask/dataframe/tests/test_shuffle.py:613: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [ts, rank]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________ test_set_index_datetime_precision[us] _____________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

unit = 'us'

    @pytest.mark.parametrize("unit", ["ns", "us"])
    def test_set_index_datetime_precision(unit):
        # https://github.com/dask/dask/issues/6864
    
        df = pd.DataFrame(
            [
                [1567703791155681, 1],
                [1567703792155681, 2],
                [1567703790155681, 0],
                [1567703793155681, 3],
            ],
            columns=["ts", "rank"],
        )
        df.ts = pd.to_datetime(df.ts, unit=unit)
        ddf = dd.from_pandas(df, npartitions=2)
        ddf = ddf.set_index("ts")
    
>       assert_eq(ddf, df.set_index("ts"))

dask/dataframe/tests/test_shuffle.py:613: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [ts, rank]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_set_index_drop[True] ___________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

drop = True

    @pytest.mark.parametrize("drop", [True, False])
    def test_set_index_drop(drop):
        pdf = pd.DataFrame(
            {
                "A": list("ABAABBABAA"),
                "B": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                "C": [1, 2, 3, 2, 1, 3, 2, 4, 2, 3],
            }
        )
        ddf = dd.from_pandas(pdf, 3)
    
>       assert_eq(ddf.set_index("A", drop=drop), pdf.set_index("A", drop=drop))

dask/dataframe/tests/test_shuffle.py:627: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B, C]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________________ test_set_index_drop[False] __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

drop = False

    @pytest.mark.parametrize("drop", [True, False])
    def test_set_index_drop(drop):
        pdf = pd.DataFrame(
            {
                "A": list("ABAABBABAA"),
                "B": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                "C": [1, 2, 3, 2, 1, 3, 2, 4, 2, 3],
            }
        )
        ddf = dd.from_pandas(pdf, 3)
    
>       assert_eq(ddf.set_index("A", drop=drop), pdf.set_index("A", drop=drop))

dask/dataframe/tests/test_shuffle.py:627: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B, C]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_set_index_empty_partition ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_set_index_empty_partition():
        test_vals = [1, 2, 3]
        converters = [int, float, str, lambda x: pd.to_datetime(x, unit="ns")]
    
        for conv in converters:
            df = pd.DataFrame(
                [{"x": conv(i), "y": i} for i in test_vals], columns=["x", "y"]
            )
            ddf = dd.concat(
                [
                    dd.from_pandas(df, npartitions=1),
                    dd.from_pandas(df[df.y > df.y.max()], npartitions=1),
                ]
            )
    
            assert any(ddf.get_partition(p).compute().empty for p in range(ddf.npartitions))
>           assert assert_eq(ddf.set_index("x"), df.set_index("x"))

dask/dataframe/tests/test_shuffle.py:721: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_set_index_on_empty[int] _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

converter = <class 'int'>

    @pytest.mark.parametrize(
        "converter", [int, float, str, lambda x: pd.to_datetime(x, unit="ns")]
    )
    def test_set_index_on_empty(converter):
        test_vals = [1, 2, 3, 4]
    
        df = pd.DataFrame([{"x": converter(x), "y": x} for x in test_vals])
        ddf = dd.from_pandas(df, npartitions=4)
    
        assert ddf.npartitions > 1
    
        actual = ddf[ddf.y > df.y.max()].set_index("x")
        expected = df[df.y > df.y.max()].set_index("x")
    
>       assert assert_eq(actual, expected, check_freq=False)

dask/dataframe/tests/test_shuffle.py:738: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_set_index_on_empty[float] ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

converter = <class 'float'>

    @pytest.mark.parametrize(
        "converter", [int, float, str, lambda x: pd.to_datetime(x, unit="ns")]
    )
    def test_set_index_on_empty(converter):
        test_vals = [1, 2, 3, 4]
    
        df = pd.DataFrame([{"x": converter(x), "y": x} for x in test_vals])
        ddf = dd.from_pandas(df, npartitions=4)
    
        assert ddf.npartitions > 1
    
        actual = ddf[ddf.y > df.y.max()].set_index("x")
        expected = df[df.y > df.y.max()].set_index("x")
    
>       assert assert_eq(actual, expected, check_freq=False)

dask/dataframe/tests/test_shuffle.py:738: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_set_index_on_empty[str] _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

converter = <class 'str'>

    @pytest.mark.parametrize(
        "converter", [int, float, str, lambda x: pd.to_datetime(x, unit="ns")]
    )
    def test_set_index_on_empty(converter):
        test_vals = [1, 2, 3, 4]
    
        df = pd.DataFrame([{"x": converter(x), "y": x} for x in test_vals])
        ddf = dd.from_pandas(df, npartitions=4)
    
        assert ddf.npartitions > 1
    
        actual = ddf[ddf.y > df.y.max()].set_index("x")
        expected = df[df.y > df.y.max()].set_index("x")
    
>       assert assert_eq(actual, expected, check_freq=False)

dask/dataframe/tests/test_shuffle.py:738: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________ test_set_index_on_empty[<lambda>] _______________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

converter = <function <lambda> at 0x7ffb8e312c40>

    @pytest.mark.parametrize(
        "converter", [int, float, str, lambda x: pd.to_datetime(x, unit="ns")]
    )
    def test_set_index_on_empty(converter):
        test_vals = [1, 2, 3, 4]
    
        df = pd.DataFrame([{"x": converter(x), "y": x} for x in test_vals])
        ddf = dd.from_pandas(df, npartitions=4)
    
        assert ddf.npartitions > 1
    
        actual = ddf[ddf.y > df.y.max()].set_index("x")
        expected = df[df.y > df.y.max()].set_index("x")
    
>       assert assert_eq(actual, expected, check_freq=False)

dask/dataframe/tests/test_shuffle.py:738: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:537: in assert_eq
    assert_divisions(a, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
____________________________ test_empty_partitions _____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_empty_partitions():
        # See https://github.com/dask/dask/issues/2408
        df = pd.DataFrame({"a": list(range(10))})
>       df["b"] = df["a"] % 3

dask/dataframe/tests/test_shuffle.py:792: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    a
0  0
1  1
2  2
3  3
4  4
5  5
6  6
7  7
8  8
9  9, key = 'b'
value = 0    0
1    1
2    2
3    0
4    1
5    2
6    0
7    1
8    2
9    0
Name: a, dtype: int64

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
___________________________ test_set_index_timestamp ___________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_set_index_timestamp():
        df = pd.DataFrame({"A": pd.date_range("2000", periods=12, tz="US/Central"), "B": 1})
        ddf = dd.from_pandas(df, 2)
        divisions = (
            pd.Timestamp("2000-01-01 00:00:00-0600", tz="US/Central"),
            pd.Timestamp("2000-01-12 00:00:00-0600", tz="US/Central"),
        )
    
        # Note: `freq` is lost during round trip
        df2 = df.set_index("A")
        ddf_new_div = ddf.set_index("A", divisions=divisions)
        for ts1, ts2 in zip(divisions, ddf_new_div.divisions):
            assert ts1.timetuple() == ts2.timetuple()
            assert ts1.tz == ts2.tz
    
>       assert_eq(df2, ddf_new_div, check_freq=False)

dask/dataframe/tests/test_shuffle.py:837: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:538: in assert_eq
    assert_divisions(b, scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:606: in assert_divisions
    ddf.enforce_runtime_divisions().compute(scheduler=scheduler)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:373: in compute
    (result,) = compute(self, traverse=False, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:910: in optimize_until
    expr = expr.lower_completely()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:499: in lower_completely
    new = expr.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:465: in lower_once
    new = operand.lower_once(lowered)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:454: in lower_once
    out = expr._lower()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1177: in _lower
    shuffled, [c for c in assigned.columns if c != "_partitions"]
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
__________________ test_disk_shuffle_check_actual_compression __________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_disk_shuffle_check_actual_compression():
        # test if the compression switch is really respected by testing the size of the actual partd-data on disk
        def generate_raw_partd_file(compression):
            # generate and write a dummy dataframe to disk and return the raw data bytes
            df1 = pd.DataFrame({"a": list(range(10000))})
            df1["b"] = (df1["a"] * 123).astype(str)
            with dask.config.set({"dataframe.shuffle.compression": compression}):
                p1 = maybe_buffered_partd(buffer=False, tempdir=None)()
                p1.append({"x": df1})
                # get underlying filename from partd - depending on nested structure of partd object
                filename = (
                    p1.partd.partd.filename("x") if compression else p1.partd.filename("x")
                )
                with open(filename, "rb") as f:
                    return f.read()
    
        # get compressed and uncompressed raw data
>       uncompressed_data = generate_raw_partd_file(compression=None)

dask/dataframe/tests/test_shuffle.py:888: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
dask/dataframe/tests/test_shuffle.py:876: in generate_raw_partd_file
    df1["b"] = (df1["a"] * 123).astype(str)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =          a
0        0
1        1
2        2
3        3
4        4
...    ...
9995  9995
9996  9996
9997  9997
9998  9998
9999  9999

[10000 rows x 1 columns]
key = 'b'
value = 0             0
1           123
2           246
3           369
4           492
         ...   
9995    1229385
9996    1229508
9997    1229631
9998    1229754
9999    1229877
Name: a, Length: 10000, dtype: object

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________________________ test_assert_eq_sorts _____________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_assert_eq_sorts():
        df = pd.DataFrame({"A": np.linspace(0, 1, 10), "B": np.random.random(10)})
        df_s = df.sort_values("B")
        assert_eq(df, df_s)
        with pytest.raises(AssertionError):
            assert_eq(df, df_s, sort_results=False)
    
        df_sr = df_s.reset_index(drop=True)
        assert_eq(df, df_sr, check_index=False)
        with pytest.raises(AssertionError):
            assert_eq(df, df_sr)
        with pytest.raises(AssertionError):
            assert_eq(df, df_sr, check_index=False, sort_results=False)
    
        ddf = dd.from_pandas(df, npartitions=2)
        ddf_s = ddf.sort_values(["B"])
>       assert_eq(df, ddf_s)

dask/dataframe/tests/test_utils_dataframe.py:585: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:544: in assert_eq
    assert_sane_keynames(b)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [A, B]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________ test_compute_current_divisions_nan_partition _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    def test_compute_current_divisions_nan_partition():
        # Compute divisions 1 null partition
        a = d[d.a > 3].sort_values("a")
>       divisions = a.compute_current_divisions("a")

dask/dataframe/tests/test_shuffle.py:972: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:2363: in compute_current_divisions
    mins, maxes, lens = _compute_partition_stats(frame, allow_overlap=set_divisions)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:6419: in _compute_partition_stats
    mins, maxes, lens = compute(mins, maxes, lens)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/base.py:678: in compute
    expr = expr.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:416: in optimize
    return optimize_until(self, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:1283: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2136: in _meta
    if is_dataframe_like(self.frame._meta):
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_sort_values[10-a-True] __________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

nelem = 10, by = 'a', ascending = True

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = dd.from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       dd.assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1084: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_sort_values[10-b-False] _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

nelem = 10, by = 'b', ascending = False

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = dd.from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       dd.assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1084: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_sort_values[10-by2-True] _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

nelem = 10, by = ['a', 'b'], ascending = True

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = dd.from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       dd.assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1084: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_________________________ test_sort_values[500-a-True] _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

nelem = 500, by = 'a', ascending = True

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = dd.from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       dd.assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1084: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_sort_values[500-b-False] _________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

nelem = 500, by = 'b', ascending = False

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = dd.from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       dd.assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1084: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________________ test_sort_values[500-by2-True] ________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

nelem = 500, by = ['a', 'b'], ascending = True

    @pytest.mark.parametrize("ascending", [True, False])
    @pytest.mark.parametrize("by", ["a", "b", ["a", "b"]])
    @pytest.mark.parametrize("nelem", [10, 500])
    def test_sort_values(nelem, by, ascending):
        np.random.seed(0)
        df = pd.DataFrame()
        df["a"] = np.ascontiguousarray(np.arange(nelem)[::-1])
        df["b"] = np.arange(100, nelem + 100)
        ddf = dd.from_pandas(df, npartitions=10)
    
        # run on single-threaded scheduler for debugging purposes
        with dask.config.set(scheduler="single-threaded"):
            got = ddf.sort_values(by=by, ascending=ascending)
        expect = df.sort_values(by=by, ascending=ascending)
>       dd.assert_eq(got, expect, check_index=False, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1084: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:317: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [a, b]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_____________ test_resample_pads_last_division_to_avoid_off_by_one _____________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

    def test_resample_pads_last_division_to_avoid_off_by_one():
        # https://github.com/dask/dask/issues/6230
        times = [
            1545362463409128000,
            1545362504369352000,
            1545362545326966000,
            1545363118769636000,
            1545363159726490000,
            1545363200687178000,
            1545363241648824000,
            1573318190393973000,
            1573318231353350000,
            1573318272313774000,
            1573318313275299000,
            1573318354233962000,
            1573318395195456000,
            1573318436154609000,
            1580687544437145000,
            1580687585394881000,
            1580687667316809000,
            1580687708275414000,
            1580687790195742000,
            1580687831154951000,
            1580687872115363000,
            1580687954035133000,
            1559127673402811000,
        ]
    
        freq = "1QE" if PANDAS_GE_220 else "1Q"
        df = pd.DataFrame({"Time": times, "Counts": range(len(times))})
>       df["Time"] = pd.to_datetime(df["Time"], utc=True)

dask/dataframe/tseries/tests/test_resample.py:130: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =                    Time  Counts
0   1545362463409128000       0
1   1545362504369352000       1
2   154536254532696600...687831154951000      19
20  1580687872115363000      20
21  1580687954035133000      21
22  1559127673402811000      22
key = 'Time'
value = 0    2018-12-21 03:21:03.409128+00:00
1    2018-12-21 03:21:44.369352+00:00
2    2018-12-21 03:22:25.326966+00:00
3   ...0:00
21   2020-02-02 23:59:14.035133+00:00
22   2019-05-29 11:01:13.402811+00:00
Name: Time, dtype: datetime64[ns, UTC]

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
________________ test_sort_values_tasks_backend[True-z-pandas] _________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

backend = 'pandas', by = 'z', ascending = True

    @pytest.mark.parametrize(
        "backend", ["pandas", pytest.param("cudf", marks=pytest.mark.gpu)]
    )
    @pytest.mark.parametrize("by", ["x", "z", ["x", "z"], ["z", "x"]])
    @pytest.mark.parametrize("ascending", [True, False])
    def test_sort_values_tasks_backend(backend, by, ascending):
        if backend == "cudf":
            pytest.importorskip("dask_cudf")
        pdf = pd.DataFrame(
            {"x": range(10), "y": [1, 2, 3, 4, 5] * 2, "z": ["cat", "dog"] * 5}
        )
        ddf = dd.from_pandas(pdf, npartitions=10)
        if backend == "cudf":
            ddf = ddf.to_backend(backend)
    
        expect = pdf.sort_values(by=by, ascending=ascending)
        got = dd.DataFrame.sort_values(
            ddf, by=by, ascending=ascending, shuffle_method="tasks"
        )
>       dd.assert_eq(got, expect, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1106: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:374: in _meta
    meta = self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
_______________ test_sort_values_tasks_backend[True-by3-pandas] ________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

backend = 'pandas', by = ['z', 'x'], ascending = True

    @pytest.mark.parametrize(
        "backend", ["pandas", pytest.param("cudf", marks=pytest.mark.gpu)]
    )
    @pytest.mark.parametrize("by", ["x", "z", ["x", "z"], ["z", "x"]])
    @pytest.mark.parametrize("ascending", [True, False])
    def test_sort_values_tasks_backend(backend, by, ascending):
        if backend == "cudf":
            pytest.importorskip("dask_cudf")
        pdf = pd.DataFrame(
            {"x": range(10), "y": [1, 2, 3, 4, 5] * 2, "z": ["cat", "dog"] * 5}
        )
        ddf = dd.from_pandas(pdf, npartitions=10)
        if backend == "cudf":
            ddf = ddf.to_backend(backend)
    
        expect = pdf.sort_values(by=by, ascending=ascending)
        got = dd.DataFrame.sort_values(
            ddf, by=by, ascending=ascending, shuffle_method="tasks"
        )
>       dd.assert_eq(got, expect, sort_results=False)

dask/dataframe/tests/test_shuffle.py:1106: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:543: in assert_eq
    assert_sane_keynames(a)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/utils.py:611: in assert_sane_keynames
    dsk = ddf.optimize()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_collection.py:572: in optimize
    return new_collection(self.expr.optimize(fuse=fuse))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:97: in optimize
    return optimize(self, **kwargs)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:891: in optimize
    return optimize_until(expr, stage)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:915: in optimize_until
    expr = expr.simplify()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:426: in simplify
    new = expr.simplify_once(dependents=dependents, simplified={})
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:396: in simplify_once
    new = operand.simplify_once(
../BUILDROOT/usr/lib/python3.14/site-packages/dask/_expr.py:369: in simplify_once
    out = expr._simplify_down()
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:2162: in _simplify_down
    str(self.frame.columns) == str(self.columns)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:451: in columns
    return list(self._meta.columns)
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_shuffle.py:374: in _meta
    meta = self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_repartition.py:50: in _meta
    return self.frame._meta
/usr/lib64/python3.14/functools.py:1127: in __get__
    val = self.func(instance)
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1940: in _meta
    return make_meta(self.operation(*args, **self._kwargs))
../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: in assign
    df[name] = val
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Empty DataFrame
Columns: [x, y, z]
Index: [], key = '_partitions'
value = Series([], dtype: int64)

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
______________________________ test_resample_agg _______________________________
[gw0] linux -- Python 3.14.0 /usr/bin/python3

df = Dask DataFrame Structure:
                       foo    bar
npartitions=4                    
2000-01-01 00:00:00  int...    ...
2000-01-01 00:09:00    ...    ...
2000-01-01 00:11:00    ...    ...
Dask Name: frompandas, 1 expression
Expr=df
pdf =                      foo  bar
2000-01-01 00:00:00    0    1
2000-01-01 00:01:00    1    1
2000-01-01 00:02:00    2    ...2000-01-01 00:08:00    8    1
2000-01-01 00:09:00    9    1
2000-01-01 00:10:00   10    1
2000-01-01 00:11:00   11    1

    def test_resample_agg(df, pdf):
        def my_sum(vals, foo=None, *, bar=None):
            return vals.sum()
    
        result = df.resample("2min").agg(my_sum, "foo", bar="bar")
        expected = pdf.resample("2min").agg(my_sum, "foo", bar="bar")
>       assert_eq(result, expected)

dask/dataframe/tseries/tests/test_resample_expr.py:112: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = Dask DataFrame Structure:
                       foo    bar
npartitions=4                    
2000-01-01 00:00:00  int...l': None}, how_args=(<function test_resample_agg.<locals>.my_sum at 0x7f5ba3047270>, 'foo'), how_kwargs={'bar': 'bar'})
b =                      foo  bar
2000-01-01 00:00:00    1    2
2000-01-01 00:02:00    5    2
2000-01-01 00:04:00    9    2
2000-01-01 00:06:00   13    2
2000-01-01 00:08:00   17    2
2000-01-01 00:10:00   21    2
serialize_graph = True, args = (), kwargs = {}
obj = Dask DataFrame Structure:
                       foo    bar
npartitions=4                    
2000-01-01 00:00:00  int...l': None}, how_args=(<function test_resample_agg.<locals>.my_sum at 0x7f5ba3047270>, 'foo'), how_kwargs={'bar': 'bar'})

    def assert_eq(a, b, *args, serialize_graph=True, **kwargs):
        if serialize_graph:
            # Check that no `Expr` instances are found in
            # the graph generated by `Expr.dask`
            with config.set({"dask-expr-no-serialize": True}):
                for obj in [a, b]:
                    if hasattr(obj, "dask"):
                        try:
>                           pickle.dumps(obj.dask)
E                           _pickle.PicklingError: Can't pickle local object <function test_resample_agg.<locals>.my_sum at 0x7f5ba3047270>
E                           when serializing list item 0
E                           when serializing tuple item 8
E                           when serializing tuple item 5
E                           when serializing dask._task_spec.Task state
E                           when serializing dask._task_spec.Task object
E                           when serializing dict item ('_resample_series-48369972c08adc46aa39776316a88611', 0)

../BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/tests/_util.py:34: PicklingError
_____________________________ test_tokenize_pandas _____________________________
[gw1] linux -- Python 3.14.0 /usr/bin/python3

    @pytest.mark.skipif("not pd")
    def test_tokenize_pandas():
        a = pd.DataFrame({"x": [1, 2, 3], "y": ["4", "asd", None]}, index=[1, 2, 3])
        b = pd.DataFrame({"x": [1, 2, 3], "y": ["4", "asd", None]}, index=[1, 2, 3])
    
        assert check_tokenize(a) == check_tokenize(b)
        b.index.name = "foo"
        assert check_tokenize(a) != check_tokenize(b)
    
        a = pd.DataFrame({"x": [1, 2, 3], "y": ["a", "b", "a"]})
        b = pd.DataFrame({"x": [1, 2, 3], "y": ["a", "b", "a"]})
>       a["z"] = a.y.astype("category")

dask/tests/test_tokenize.py:572: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self =    x  y
0  1  a
1  2  b
2  3  a, key = 'z'
value = 0    a
1    b
2    a
Name: y, dtype: category
Categories (2, object): ['a', 'b']

    def __setitem__(self, key, value) -> None:
        if not PYPY and using_copy_on_write():
            if sys.getrefcount(self) <= 3:
                warnings.warn(
                    _chained_assignment_msg, ChainedAssignmentError, stacklevel=2
                )
        elif not PYPY and not using_copy_on_write():
            if sys.getrefcount(self) <= 3 and (
                warn_copy_on_write()
                or (
                    not warn_copy_on_write()
                    and any(b.refs.has_reference() for b in self._mgr.blocks)  # type: ignore[union-attr]
                )
            ):
>               warnings.warn(
                    _chained_assignment_warning_msg, FutureWarning, stacklevel=2
                )
E               FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
E               You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
E               A typical example is when you are setting values in a column of a DataFrame, like:
E               
E               df["col"][row_indexer] = value
E               
E               Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
E               
E               See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/lib64/python3.14/site-packages/pandas/core/frame.py:4285: FutureWarning
=============================== warnings summary ===============================
dask/array/tests/test_reductions.py::test_nan_object[min]
dask/array/tests/test_reductions.py::test_nan_object[min]
dask/array/tests/test_reductions.py::test_nan_object[min]
dask/array/tests/test_reductions.py::test_nan_object[max]
dask/array/tests/test_reductions.py::test_nan_object[max]
dask/array/tests/test_reductions.py::test_nan_object[max]
  /usr/lib64/python3.14/site-packages/numpy/_core/fromnumeric.py:86: RuntimeWarning: invalid value encountered in reduce
    return ufunc.reduce(obj, axis, dtype, out, **passkwargs)

dask/bytes/tests/test_http.py::test_simple
  /usr/lib/python3.14/site-packages/fsspec/asyn.py:125: DeprecationWarning: 'asyncio.get_event_loop_policy' is deprecated and slated for removal in Python 3.16
    original_policy = asyncio.get_event_loop_policy()

dask/bytes/tests/test_http.py::test_simple
  /usr/lib/python3.14/site-packages/fsspec/asyn.py:132: DeprecationWarning: 'asyncio.set_event_loop_policy' is deprecated and slated for removal in Python 3.16
    asyncio.set_event_loop_policy(original_policy)

dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_apply_args
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_apply_args
dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_apply_args
  /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/groupby.py:116: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
    return g.apply(func, *args, **kwargs)

dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_apply_args
  /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/dask/dataframe/dask_expr/tests/test_groupby.py:655: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
    pdf.groupby("x").apply(lambda x, y: x + y, 1),

dask/dataframe/io/tests/test_sql.py::test_to_sql[False-1]
  /usr/lib/python3.14/site-packages/_pytest/unraisableexception.py:85: PytestUnraisableExceptionWarning: Exception ignored while finalizing database connection <sqlite3.Connection object at 0x7f5bb951d990>: None
  
  Traceback (most recent call last):
    File "/usr/lib64/python3.14/site-packages/pandas/core/indexes/api.py", line 265, in _find_common_index_dtype
      def _find_common_index_dtype(inds):
      
  ResourceWarning: unclosed database in <sqlite3.Connection object at 0x7f5bb951d990>
  
    warnings.warn(pytest.PytestUnraisableExceptionWarning(msg))

dask/dataframe/io/tests/test_sql.py::test_to_sql[True-1]
  /usr/lib/python3.14/site-packages/_pytest/unraisableexception.py:85: PytestUnraisableExceptionWarning: Exception ignored while finalizing database connection <sqlite3.Connection object at 0x7f5bab3072e0>: None
  
  Traceback (most recent call last):
    File "/usr/lib64/python3.14/site-packages/pandas/core/indexes/base.py", line 1671, in name
      @property
      
  ResourceWarning: unclosed database in <sqlite3.Connection object at 0x7f5bab3072e0>
  
    warnings.warn(pytest.PytestUnraisableExceptionWarning(msg))

dask/dataframe/tests/test_arithmetics_reduction.py::test_frame_series_arithmetic_methods
  /usr/lib/python3.14/site-packages/_pytest/unraisableexception.py:85: PytestUnraisableExceptionWarning: Exception ignored while finalizing database connection <sqlite3.Connection object at 0x7f5bab1b13f0>: None
  
  Traceback (most recent call last):
    File "/builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/local.py", line 203, in start_state_from_dask
      _wait = task.dependencies - set(cache)
                                  ~~~^^^^^^^
  ResourceWarning: unclosed database in <sqlite3.Connection object at 0x7f5bab1b13f0>
  
    warnings.warn(pytest.PytestUnraisableExceptionWarning(msg))

dask/dataframe/dask_expr/tests/test_groupby.py: 13 warnings
  /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/dask/dataframe/dask_expr/tests/test_groupby.py:399: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
  You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
  A typical example is when you are setting values in a column of a DataFrame, like:
  
  df["col"][row_indexer] = value
  
  Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
  
  See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
    x["new"] = x.sum().sum()

dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-True]
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-True]
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-True]
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-True]
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-True]
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-True]
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-True]
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-True]
  /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: ChainedAssignmentError: A value is trying to be set on a copy of a DataFrame or Series through chained assignment.
  When using the Copy-on-Write mode, such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy.
  
  Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.
  
  See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
    df[name] = val

dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-True]
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-True]
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-True]
dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-True]
  /usr/lib64/python3.14/site-packages/pandas/core/frame.py:5239: ChainedAssignmentError: A value is trying to be set on a copy of a DataFrame or Series through chained assignment.
  When using the Copy-on-Write mode, such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy.
  
  Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.
  
  See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
    data[k] = com.apply_if_callable(v, data)

dask/dataframe/dask_expr/tests/test_groupby.py: 8 warnings
dask/dataframe/tests/test_groupby.py: 448 warnings
dask/dataframe/tests/test_shuffle.py: 6 warnings
  /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/dask_expr/_expr.py:1908: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
  You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
  A typical example is when you are setting values in a column of a DataFrame, like:
  
  df["col"][row_indexer] = value
  
  Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
  
  See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
    df[name] = val

dask/dataframe/tests/test_groupby.py: 56 warnings
  /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/groupby.py:140: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
    return g.apply(func, *args, **kwargs)

dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[disk-sum0]
dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[tasks-sum0]
  /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/dask/dataframe/tests/test_groupby.py:2272: FutureWarning: The provided callable <function sum at 0x7f5c15d06820> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
    pdf.groupby("A").transform(transformation),

dask/dataframe/tests/test_groupby.py: 14 warnings
  /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/groupby.py:142: FutureWarning: The provided callable <function sum at 0x7f5c15d06820> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
    return g.transform(func, *args, **kwargs)

dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[disk-sum0]
dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[tasks-sum0]
  /usr/lib64/python3.14/site-packages/numpy/_core/fromnumeric.py:84: FutureWarning: The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)
    return reduction(axis=axis, out=out, **passkwargs)

dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[disk-sum0]
dask/dataframe/tests/test_groupby.py::test_groupby_transform_funcs[tasks-sum0]
  /builddir/build/BUILD/python-dask-2025.4.1-build/dask-2025.4.1/dask/dataframe/tests/test_groupby.py:2278: FutureWarning: The provided callable <function sum at 0x7f5c15d06820> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
    pdf.groupby("A")["B"].transform(transformation),

dask/dataframe/tests/test_groupby.py: 14 warnings
  /builddir/build/BUILD/python-dask-2025.4.1-build/BUILDROOT/usr/lib/python3.14/site-packages/dask/dataframe/groupby.py:142: FutureWarning: The provided callable <function sum at 0x7f5c15d06820> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
    return g.transform(func, *args, **kwargs)

dask/tests/test_multiprocessing.py::test_custom_context_used_python3_posix
dask/tests/test_multiprocessing.py::test_custom_context_used_python3_posix
  /usr/lib64/python3.14/multiprocessing/popen_fork.py:70: DeprecationWarning: This process (pid=4103) is multi-threaded, use of fork() may lead to deadlocks in the child.
    self.pid = os.fork()

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
============================= slowest 10 durations =============================
9.17s call     dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[disk-False]
8.87s call     dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[tasks-False]
7.76s call     dask/dataframe/tests/test_arithmetics_reduction.py::test_frame_series_arithmetic_methods
7.32s call     dask/dataframe/io/tests/test_io.py::test_to_bag
6.32s call     dask/dataframe/dask_expr/tests/test_groupby.py::test_split_out_automatically
5.36s call     dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[disk-True]
5.34s call     dask/dataframe/tests/test_groupby.py::test_split_apply_combine_on_series[tasks-True]
4.01s call     dask/dataframe/tests/test_dataframe.py::test_repartition
3.86s call     dask/dataframe/dask_expr/tests/test_concat.py::test_concat5
3.49s call     dask/dataframe/tests/test_multi.py::test_concat5
=========================== short test summary info ============================
SKIPPED [1] dask/array/tests/test_cupy_core.py:14: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_cupy_creation.py:13: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_cupy_gufunc.py:12: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_cupy_linalg.py:12: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_cupy_overlap.py:12: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_cupy_percentile.py:11: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_cupy_random.py:13: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_cupy_reductions.py:14: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_cupy_routines.py:12: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_cupy_slicing.py:12: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_cupy_sparse.py:10: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_image.py:8: could not import 'skimage': No module named 'skimage'
SKIPPED [1] dask/array/tests/test_sparse.py:12: could not import 'sparse': No module named 'sparse'
SKIPPED [1] dask/array/tests/test_xarray.py:13: could not import 'xarray': No module named 'xarray'
SKIPPED [1] dask/bytes/tests/test_s3.py:14: could not import 's3fs': No module named 's3fs'
SKIPPED [1] dask/dataframe/dask_expr/io/tests/test_distributed.py:7: could not import 'distributed': No module named 'distributed'
SKIPPED [1] dask/dataframe/dask_expr/tests/test_diagnostics.py:7: could not import 'distributed': No module named 'distributed'
SKIPPED [1] dask/dataframe/dask_expr/tests/test_distributed.py:12: could not import 'distributed': No module named 'distributed'
SKIPPED [1] dask/tests/test_cache.py:13: could not import 'cachey': No module named 'cachey'
SKIPPED [1] dask/tests/test_distributed.py:5: could not import 'distributed': No module named 'distributed'
SKIPPED [1] dask/tests/test_dot.py:12: could not import 'ipycytoscape': No module named 'ipycytoscape'
SKIPPED [1] dask/tests/test_layers.py:7: could not import 'distributed': No module named 'distributed'
SKIPPED [1] dask/tests/test_spark_compat.py:13: could not import 'pyspark': No module named 'pyspark'
SKIPPED [1] dask/widgets/tests/test_widgets.py:7: could not import 'jinja2': No module named 'jinja2'
SKIPPED [15] conftest.py:66: need --runarrayexpr option to run
SKIPPED [60] dask/array/tests/test_creation.py:924: Bug when pad_width is larger than dimension: https://github.com/dask/dask/issues/5303
SKIPPED [20] dask/array/tests/test_creation.py:924: Not implemented
SKIPPED [20] dask/array/tests/test_creation.py:924: Empty leads to undefined values, which may be different
SKIPPED [1] dask/array/tests/test_array_core.py:1976: could not import 'sparse': No module named 'sparse'
SKIPPED [1] dask/array/tests/test_array_core.py:1982: could not import 'jinja2': No module named 'jinja2'
SKIPPED [1] dask/array/tests/test_array_core.py:3897: could not import 'distributed': No module named 'distributed'
SKIPPED [1] dask/array/tests/test_array_core.py:5148: could not import 'tiledb': No module named 'tiledb'
SKIPPED [1] dask/array/tests/test_array_core.py:5182: could not import 'tiledb': No module named 'tiledb'
SKIPPED [6] dask/array/tests/test_array_core.py:5381: could not import 'cupyx.scipy.sparse': No module named 'cupyx'
SKIPPED [1] dask/array/tests/test_gufunc.py:618: could not import 'numba': No module named 'numba'
SKIPPED [1] dask/array/tests/test_gufunc.py:641: could not import 'numba': No module named 'numba'
SKIPPED [1] dask/array/tests/test_gufunc.py:657: could not import 'sparse': No module named 'sparse'
SKIPPED [1] dask/array/tests/test_array_core.py:5596: could not import 'sparse': No module named 'sparse'
SKIPPED [1] dask/array/tests/test_array_core.py:5756: array roundtrips don't work yet
SKIPPED [1] dask/array/tests/test_array_core.py:5881: could not import 'xarray': No module named 'xarray'
SKIPPED [3] dask/array/tests/test_array_function.py:105: could not import 'sparse': No module named 'sparse'
SKIPPED [1] dask/array/tests/test_array_function.py:117: could not import 'sparse': No module named 'sparse'
SKIPPED [2] dask/array/tests/test_array_function.py:134: could not import 'cupy': No module named 'cupy'
SKIPPED [288] dask/array/tests/test_creation.py:49: could not import 'cupy': No module named 'cupy'
SKIPPED [198] conftest.py:64: need --runslow option to run
SKIPPED [1] dask/array/tests/test_overlap.py:979: could not import 'xarray': No module named 'xarray'
SKIPPED [1] dask/array/tests/test_percentiles.py:62: unconditional skip
SKIPPED [1] dask/array/tests/test_random.py:431: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/array/tests/test_reductions.py:835: could not import 'xarray': No module named 'xarray'
SKIPPED [4] dask/array/tests/test_routines.py:2103: Skipped
SKIPPED [1] dask/array/tests/test_slicing.py:439: really long test
SKIPPED [1] dask/array/tests/test_svg.py:27: could not import 'jinja2': No module named 'jinja2'
SKIPPED [1] dask/array/tests/test_svg.py:61: could not import 'jinja2': No module named 'jinja2'
SKIPPED [1] dask/array/tests/test_ufunc.py:143: fix calls floor in a way that we do not yet support
SKIPPED [4] dask/bag/tests/test_text.py:49: compress function not provided for lzma
SKIPPED [4] dask/bag/tests/test_text.py:49: compress function not provided for snappy
SKIPPED [2] dask/bytes/tests/test_compression.py:14: compression function not provided
SKIPPED [4] dask/bytes/tests/test_local.py:198: compression function not provided
SKIPPED [4] dask/bytes/tests/test_local.py:249: compression function not provided
SKIPPED [16] dask/dataframe/dask_expr/tests/test_merge.py:628: Runtime still slower than I would like, so save some time
SKIPPED [2] dask/dataframe/io/tests/test_csv.py:772: compress function not provided for lzma
SKIPPED [2] dask/dataframe/io/tests/test_csv.py:772: compress function not provided for snappy
SKIPPED [1] dask/dataframe/io/tests/test_csv.py:793: unconditional skip
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:26: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:57: segfaults due to https://github.com/PyTables/PyTables/issues/977
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:141: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:234: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:282: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:339: segfaults due to https://github.com/PyTables/PyTables/issues/977
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:413: No need to run with pyarrow strings
SKIPPED [9] dask/dataframe/io/tests/test_hdf.py:433: segfaults due to https://github.com/PyTables/PyTables/issues/977
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:504: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:520: No need to run with pyarrow strings
SKIPPED [2] dask/dataframe/io/tests/test_hdf.py:577: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:621: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:634: segfaults due to https://github.com/PyTables/PyTables/issues/977
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:691: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:709: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:746: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:766: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:779: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:793: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:805: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:816: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:830: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_hdf.py:872: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_io.py:318: could not import 'cudf': No module named 'cudf'
SKIPPED [1] dask/dataframe/io/tests/test_io.py:520: could not import 'cupy': No module named 'cupy'
SKIPPED [1] dask/dataframe/io/tests/test_io.py:968: could not import 'cudf': No module named 'cudf'
SKIPPED [1] dask/dataframe/io/tests/test_io.py:986: could not import 'cudf': No module named 'cudf'
SKIPPED [1] dask/dataframe/io/tests/test_parquet.py:85: pyarrow are installed
SKIPPED [1] dask/dataframe/io/tests/test_parquet.py:894: No need to run with pyarrow strings
SKIPPED [25] dask/dataframe/io/tests/test_parquet.py:1001: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_parquet.py:1136: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/io/tests/test_parquet.py:3792: could not import 'cudf': No module named 'cudf'
SKIPPED [1] dask/dataframe/io/tests/test_parquet.py:4047: could not import 'botocore': No module named 'botocore'
SKIPPED [1] dask/dataframe/io/tests/test_sql.py:117: Requires a postgres server. Sqlite does not support multiple schemas.
SKIPPED [1] dask/dataframe/tests/test_accessors.py:52: from_pandas doesn't support Index
SKIPPED [1] dask/dataframe/dask_expr/tests/test_format.py:18: could not import 'jinja2': No module named 'jinja2'
SKIPPED [1] dask/dataframe/dask_expr/tests/test_format.py:176: could not import 'jinja2': No module named 'jinja2'
SKIPPED [2] dask/dataframe/tests/test_arithmetics_reduction.py:596: numpy>2 removed product -- users should move to prod
SKIPPED [1] dask/dataframe/tests/test_categorical.py:123: could not import 'dask_cudf': No module named 'dask_cudf'
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:2931: Available at 2.1
SKIPPED [3] dask/dataframe/tests/test_dataframe.py:3033: https://github.com/rapidsai/cudf/issues/16560
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:3106: https://github.com/rapidsai/cudf/issues/16560
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:4260: could not import 'cudf': No module named 'cudf'
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:4491: could not import 'sparse': No module named 'sparse'
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:4568: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:4808: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:4820: No need to run with pyarrow strings
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:4877: not important now
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:4887: not important now
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:5280: could not import 'cudf': No module named 'cudf'
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:5311: could not import 'xarray': No module named 'xarray'
SKIPPED [1] dask/dataframe/tests/test_dataframe.py:5329: could not import 'xarray': No module named 'xarray'
SKIPPED [4] dask/dataframe/tests/test_groupby.py:689: https://github.com/dask/dask/issues/10034
SKIPPED [1] dask/dataframe/tests/test_groupby.py:849: Tasks-only shuffle test
SKIPPED [6] dask/dataframe/tests/test_groupby.py:2105: No need to run with pyarrow strings
SKIPPED [108] dask/dataframe/tests/test_groupby.py:2490: could not import 'cudf': No module named 'cudf'
SKIPPED [4] dask/dataframe/tests/test_groupby.py:2521: could not import 'cudf': No module named 'cudf'
SKIPPED [4] dask/dataframe/tests/test_groupby.py:2558: could not import 'dask_cudf': No module named 'dask_cudf'
SKIPPED [2] dask/dataframe/tests/test_groupby.py:2659: could not import 'cudf': No module named 'cudf'
SKIPPED [48] dask/dataframe/tests/test_groupby.py:2814: Gives zeros rather than nans.
SKIPPED [16] dask/dataframe/tests/test_groupby.py:2810: Can't calculate median over categorical
SKIPPED [16] dask/dataframe/tests/test_groupby.py:2812: Can't deal with unobserved cats in median at the moment
SKIPPED [20] dask/dataframe/tests/test_groupby.py:1145: https://github.com/dask/dask/issues/9509
SKIPPED [1] dask/dataframe/tests/test_indexing.py:648: could not import 'cudf': No module named 'cudf'
SKIPPED [2] dask/dataframe/tests/test_merge_column_and_index.py:260: breaks with pandas=2.1.0+
SKIPPED [6] dask/dataframe/tests/test_multi.py:871: could not import 'cudf': No module named 'cudf'
SKIPPED [8] dask/dataframe/tests/test_multi.py:1191: breaks with pandas=2.1.0+
SKIPPED [32] dask/dataframe/tests/test_groupby.py:2816: Can't calculate observed with all nans
SKIPPED [1] dask/dataframe/tests/test_multi.py:2150: could not import 'cudf': No module named 'cudf'
SKIPPED [96] dask/dataframe/tests/test_groupby.py:2808: Not implemented for DataFrameGroupBy yet.
SKIPPED [12] dask/dataframe/tests/test_groupby.py:3068: https://github.com/dask/dask/issues/9882
SKIPPED [8] dask/dataframe/tests/test_shuffle.py:1094: could not import 'dask_cudf': No module named 'dask_cudf'
SKIPPED [1] dask/dataframe/tests/test_rolling.py:476: could not import 'numba': No module named 'numba'
SKIPPED [1] dask/dataframe/tests/test_shuffle.py:501: could not import 'dask_cudf': No module named 'dask_cudf'
SKIPPED [1] dask/dataframe/tests/test_shuffle.py:539: could not import 'cudf': No module named 'cudf'
SKIPPED [1] dask/dataframe/tests/test_shuffle.py:568: could not import 'cudf': No module named 'cudf'
SKIPPED [3] dask/dataframe/tests/test_ufunc.py:532: Numpy started overflowing while we are casting to float
SKIPPED [1] dask/dataframe/tests/test_ufunc.py:181: fix calls floor in a way that we do not yet support
SKIPPED [1] dask/diagnostics/tests/test_profiler.py:180: condition: not bokeh
SKIPPED [1] dask/diagnostics/tests/test_profiler.py:197: condition: not bokeh
SKIPPED [1] dask/diagnostics/tests/test_profiler.py:225: condition: not bokeh
SKIPPED [1] dask/diagnostics/tests/test_profiler.py:266: condition: not bokeh
SKIPPED [1] dask/diagnostics/tests/test_profiler.py:295: condition: not bokeh
SKIPPED [1] dask/diagnostics/tests/test_profiler.py:303: condition: not bokeh
SKIPPED [1] dask/diagnostics/tests/test_profiler.py:331: condition: not bokeh
SKIPPED [1] dask/diagnostics/tests/test_profiler.py:344: condition: not bokeh
SKIPPED [1] dask/diagnostics/tests/test_profiler.py:363: condition: not bokeh
SKIPPED [1] dask/diagnostics/tests/test_progress.py:100: could not import 'cachey': No module named 'cachey'
SKIPPED [1] dask/tests/test_backends.py:13: could not import 'cudf': No module named 'cudf'
SKIPPED [1] dask/tests/test_base.py:535: could not import 'ipycytoscape': No module named 'ipycytoscape'
SKIPPED [1] dask/tests/test_ci.py:11: Only check for dev packages in `upstream` CI build
SKIPPED [1] dask/tests/test_delayed.py:935: could not import 'distributed': No module named 'distributed'
SKIPPED [1] dask/tests/test_datasets.py:9: could not import 'mimesis': No module named 'mimesis'
SKIPPED [1] dask/tests/test_datasets.py:18: could not import 'mimesis': No module named 'mimesis'
SKIPPED [1] dask/tests/test_datasets.py:24: could not import 'mimesis': No module named 'mimesis'
SKIPPED [1] dask/tests/test_datasets.py:43: could not import 'mimesis': No module named 'mimesis'
SKIPPED [1] dask/tests/test_highgraph.py:140: could not import 'jinja2': No module named 'jinja2'
SKIPPED [1] dask/tests/test_imports.py:43: could not import 'bokeh': No module named 'bokeh'
SKIPPED [1] dask/tests/test_imports.py:48: could not import 'distributed': No module named 'distributed'
SKIPPED [2] dask/tests/test_order.py:1396: could not import 'xarray': No module named 'xarray'
SKIPPED [1] dask/tests/test_order.py:1433: could not import 'xarray': No module named 'xarray'
SKIPPED [1] dask/tests/test_order.py:2345: could not import 'xarray': No module named 'xarray'
SKIPPED [1] dask/tests/test_sizeof.py:257: could not import 'xarray': No module named 'xarray'
SKIPPED [1] dask/tests/test_sizeof.py:279: could not import 'xarray': No module named 'xarray'
SKIPPED [1] dask/tests/test_task_spec.py:354: could not import 'distributed': No module named 'distributed'
SKIPPED [1] dask/tests/test_task_spec.py:391: could not import 'distributed': No module named 'distributed'
SKIPPED [1] dask/tests/test_tokenize.py:961: https://github.com/dask/dask/issues/11457
SKIPPED [1] dask/tests/test_multiprocessing.py:287: POSIX supports different contexts
SKIPPED [1] dask/tests/test_multiprocessing.py:300: POSIX supports different contexts
SKIPPED [5] dask/tests/test_tokenize.py:1376: condition: not numba
SKIPPED [1] dask/tests/test_tokenize.py:1389: condition: not numba
SKIPPED [1] dask/tests/test_tokenize.py:1395: condition: not numba
SKIPPED [1] dask/tests/test_traceback.py:113: could not import 'distributed': No module named 'distributed'
SKIPPED [1] dask/tests/test_utils.py:464: could not import 'multipledispatch': No module named 'multipledispatch'
SKIPPED [1] dask/tests/test_utils.py:934: could not import 'cupy': No module named 'cupy'
FAILED dask/bag/tests/test_bag.py::test_multiple_repartition_partition_size - dask.multiprocessing.TypeError: cannot pickle 'itertools.chain' object
FAILED dask/dataframe/dask_expr/io/tests/test_io.py::test_combine_similar[parquet-read_parquet-ReadParquet] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/io/tests/test_io.py::test_combine_similar[csv-read_csv-FromMap] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/io/tests/test_io.py::test_combine_similar_no_projection_on_one_branch - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/io/tests/test_io.py::test_map_partitions_assign_fusedio - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_to_parquet[True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_to_parquet[False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_timestamp_divisions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/io/tests/test_parquet.py::test_read_parquet_index_projection - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_align_partitions.py::test_assign_align_partitions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_categorical.py::test_categorize - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_categorical.py::test_categorical_set_index - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_categorical.py::test_categorize_drops_category_columns - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_dot - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_numeric_column_names - _pickle.PicklingError: Can't pickle local object <function test_numeric_col...
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_divisions - _pickle.PicklingError: Can't pickle local object <function test_apply_divis...
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_apply_args - _pickle.PicklingError: Can't pickle local object <function test_groupby_app...
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_udf_user_warning - _pickle.PicklingError: Can't pickle local object <function test_groupby_udf...
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_median_series - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-a-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumsum-key1-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-a-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumprod-key1-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-a-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_cumulative[cumcount-key1-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumsum-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumsum-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumprod-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumprod-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumcount-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[cumcount-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_dataframe_groupby_agg_custom_sum - _pickle.PicklingError: Can't pickle local object <function test_dataframe_g...
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_getitem_apply_group_keys - _pickle.PicklingError: Can't pickle local object <function test_groupby_get...
FAILED dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_merge[True] - _pickle.PicklingError: Can't pickle local object <function test_map_partiti...
FAILED dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_merge[False] - _pickle.PicklingError: Can't pickle local object <function test_map_partiti...
FAILED dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_provide_meta - _pickle.PicklingError: Can't pickle local object <function test_map_overlap...
FAILED dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_propagates_index_metadata - _pickle.PicklingError: Can't pickle local object <function test_map_partiti...
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_join[tasks-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_join[tasks-outer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_join[disk-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_join[disk-outer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_singleton_divisions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_combine_similar[2-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_combine_similar[1-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_combine_similar_intermediate_projections - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_categorical_merge_with_merge_column_cat_in_one_and_not_other_upcasts - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_empty_left_df[right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_empty_left_df[outer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-inner] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-outer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-left] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_columns_dtypes1[True-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_merge_avoid_overeager_filter_pushdown - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_merge.py::test_mismatching_meta - _pickle.PicklingError: Can't pickle local object <function test_mismatching...
FAILED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>6] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_partitioning_knowledge.py::test_avoid_shuffle_when_possible[<lambda>7] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_method - _pickle.PicklingError: Can't pickle local object <function test_reduction_m...
FAILED dask/dataframe/dask_expr/tests/test_reductions.py::test_reduction_split_every_false - _pickle.PicklingError: Can't pickle local object <function test_reduction_s...
FAILED dask/dataframe/dask_expr/tests/test_reductions.py::test_cat_value_counts_large_unknown_categories - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_repartition.py::test_repartition_combine_similar[kwargs3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table[last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_reshape.py::test_pivot_table_fails - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[1-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_agg...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[1-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_agg...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[2-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_agg...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_agg[2-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_agg...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-1-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-1-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-2-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-True-2-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-1-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-1-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-2-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-1-False-2-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-1-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-1-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-2-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-True-2-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-1-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-1-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-2-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[2-None-False-2-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-1-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-1-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-2-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-True-2-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-1-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-1-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-2-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-1-False-2-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-1-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-1-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-2-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-True-2-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-1-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-1-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-2-1] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_apply[None-None-False-2-2] - _pickle.PicklingError: Can't pickle local object <function test_rolling_app...
FAILED dask/dataframe/dask_expr/tests/test_rolling.py::test_rolling_one_element_window_empty_after - _pickle.PicklingError: Can't pickle local object <function test_rolling_one...
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[32-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[32-12] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[8-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_task_shuffle_index[8-12] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-a-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-b-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[10-by2-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-a-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-b-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_[500-by2-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[1.0-128000000.0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[1.0-12800000.0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[2.0-128000000.0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index[2.0-12800000.0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_sorted - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_pre_sorted - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[True-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[True-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[False-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_no_sort[False-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_repartition - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_simplify - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_numeric_columns - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values[None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values[tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_optimize - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_list - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_descending - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_head_nlargest_string - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_head_nlargest - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_head_nlargest_string - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_filter_sort - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_add - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_with_dask_dt_index[-16h] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_sort_values_shuffle_options - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_predicate_pushdown - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_with_explicit_divisions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_npartitions_changes - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_empty_partitions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_user_divisions_one_partition - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_divisions_npartitions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_sort_values_unordered_categorical - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_before_assign - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_shuffle.py::test_set_index_shuffle_afterwards - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_dtype_coercion[read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_dtype_coercion[read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_pandas_read_text_dtype_coercion[read_fwf-files2] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_simple[read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_simple[read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_simple[read_fwf-files2] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_kwargs[read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_kwargs[read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_kwargs[read_fwf-files2] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_blocked[read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_blocked[read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_text_blocks_to_pandas_blocked[read_fwf-files2] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_skiprows[read_csv-read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_skiprows[read_table-read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_comment[read_csv-read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_comment[read_table-read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_skipfooter[read_csv-read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_skipfooter[read_table-read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_skiprows_as_list[read_csv-read_csv-files0-str, int, int\n] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_skiprows_as_list[read_table-read_table-files1-str\t int\t int\n] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_enforce_dtypes[read_csv-blocks0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_enforce_dtypes[read_table-blocks1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_enforce_columns[read_csv-blocks0] - Failed: DID NOT RAISE <class 'ValueError'>
FAILED dask/dataframe/io/tests/test_csv.py::test_enforce_columns[read_table-blocks1] - Failed: DID NOT RAISE <class 'ValueError'>
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_files[read_csv-read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_files[read_table-read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_files_list[read_csv-read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_files_list[read_table-read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column[read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column[read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_as_str[read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_as_str[read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_is_dtype_category[read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_is_dtype_category[read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_with_multiple_partitions_per_file[read_csv-files0] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_include_path_column_with_multiple_partitions_per_file[read_table-files1] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_index - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_csv.py::test_usecols - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_consistent_dtypes - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_categorical_dtypes - FutureWarning: An error occurred while calling the read_csv method register...
FAILED dask/dataframe/io/tests/test_csv.py::test_categorical_known - FutureWarning: An error occurred while calling the read_csv method register...
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None0-None] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None0-10] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[zip-None] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[zip-10] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[bz2-None] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[bz2-10] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[gzip-None] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[gzip-10] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[xz-None] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[xz-10] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None1-None] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_read_csv_compression[None1-10] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_header_int[3] - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_assume_missing - FutureWarning: An error occurred while calling the read_csv method register...
FAILED dask/dataframe/io/tests/test_csv.py::test_robust_column_mismatch - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_different_columns_are_allowed - ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
FAILED dask/dataframe/io/tests/test_csv.py::test_reading_empty_csv_files_with_path - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[split] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[records] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_column[values] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_json.py::test_read_json_with_path_converter - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_to_datetime_timezone - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_categorical[pyarrow-pyarrow] - FutureWarning: An error occurred while calling the read_parquet method regi...
FAILED dask/dataframe/io/tests/test_parquet.py::test_append_with_partition - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_partition_on_cats_2[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_categories[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_inference[infer-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_schema_inference[complex-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_partition_on[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_partition_on_duplicates[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_partition_on_string[aa] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_partition_on_string[partition_on1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_filters_categorical[pyarrow-pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_parquet_select_cats[pyarrow] - FutureWarning: An error occurred while calling the read_parquet method regi...
FAILED dask/dataframe/io/tests/test_parquet.py::test_writing_parquet_with_kwargs[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_select_partitioned_column[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_arrow_partitioning - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_append_cat_fp[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df3] - FutureWarning: An error occurred while calling the read_parquet method regi...
FAILED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_arrow[df4] - FutureWarning: An error occurred while calling the read_parquet method regi...
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_columns_assignment - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_parent_mixed_column_assignment - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_setitem_triggering_realign - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_timeseries_nulls_in_schema[pyarrow-infer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_timeseries_nulls_in_schema[pyarrow-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_apply_infer_columns - _pickle.PicklingError: Can't pickle local object <function test_apply_infer...
FAILED dask/dataframe/io/tests/test_parquet.py::test_filter_nonpartition_columns[pyarrow-pyarrow-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_filter_nonpartition_columns[pyarrow-pyarrow-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_partitioned_no_pandas_metadata[pyarrow-write_cols0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_partitioned_no_pandas_metadata[pyarrow-write_cols1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_directory_partitioning - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_partitioned[pyarrow-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_partitioned[pyarrow-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_read_from_paths - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_filter_partitioned[True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_filter_partitioned[False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_pyarrow_dataset_filter_on_partitioned[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_to_parquet_overwrite_files_from_read_parquet_in_same_call_raises[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_avoid_alignment - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_dir_filter[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_extra_file[pyarrow-None] - FutureWarning: An error occurred while calling the read_parquet method regi...
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_replace_filtered_combine_similar - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_custom_filename_with_partition[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_parquet.py::test_roundtrip_partitioned_pyarrow_dataset[pyarrow] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_combine - _pickle.PicklingError: Can't pickle local object <function test_combine.<lo...
FAILED dask/dataframe/io/tests/test_parquet.py::test_read_parquet_preserve_categorical_column_dtype - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_sql.py::test_query_index_from_query - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_sql.py::test_to_sql[False-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/io/tests/test_sql.py::test_to_sql[True-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_assign_projection_mix - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_empty_from_pandas_projection - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_binop_scalar_left - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_collection.py::test_align_known_divisions_in_assign - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_concat.py::test_concat_ignore_order - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_fusion.py::test_optimize_fusion_many - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_reduction_optimize - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[False-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[False-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_group_keys[True] - _pickle.PicklingError: Can't pickle local object <function test_groupby_gro...
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_group_keys[False] - _pickle.PicklingError: Can't pickle local object <function test_groupby_gro...
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_group_keys[None] - _pickle.PicklingError: Can't pickle local object <function test_groupby_gro...
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_creates_copy_cols[None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-False-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-False-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-True-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[False-True-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-False-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-False-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-True-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_with_larger_dataset[None-True-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[False-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[False-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[None-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_arithmetics_reduction.py::test_datetime_std_across_axis1_null_results[None-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_apply - _pickle.PicklingError: Can't pickle local object <function test_groupby_app...
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_transform - _pickle.PicklingError: Can't pickle local object <function test_groupby_tra...
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_groupby_numeric_only_lambda_caller - _pickle.PicklingError: Can't pickle local object <function test_groupby_num...
FAILED dask/dataframe/tests/test_categorical.py::test_categorize - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index[disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index[tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[1-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[1-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[1-6] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[3-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[3-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-True] - _pickle.PicklingError: Can't pickle local object <function <lambda>.<locals...
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>0-False] - _pickle.PicklingError: Can't pickle local object <function <lambda>.<locals...
FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[3-6] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-True] - _pickle.PicklingError: Can't pickle local object <function <lambda>.<locals...
FAILED dask/dataframe/dask_expr/tests/test_groupby.py::test_apply_or_transform_shuffle_multilevel[<lambda>1-False] - _pickle.PicklingError: Can't pickle local object <function <lambda>.<locals...
FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[6-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[6-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_categorical_set_index_npartitions_vs_ncategories[6-6] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_repartition_on_categoricals[1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_repartition_on_categoricals[4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_categorize_nan - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_categorical.py::test_return_type_known_categories - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-None-None-subset4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_describe[all-None-None-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_describe[include6-None-percentiles6-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_describe[include7-None-None-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_describe[include8-None-percentiles8-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_describe[None-exclude9-None-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_describe[include10-None-None-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-True-<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[disk-True-<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-True-<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_get_group[tasks-True-<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumsum-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumsum-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumprod-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumprod-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumcount-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[disk-cumcount-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumsum-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumsum-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumprod-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumprod-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumcount-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_assign - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_cumfunc_with_named_index[tasks-cumcount-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_assign_callable - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_assign_dtypes - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_assign_pandas_series - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_fillna_duplicate_index - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_fillna_series_types - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_select_dtypes[include0-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_select_dtypes[None-exclude1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_select_dtypes[include2-exclude2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_select_dtypes[include3-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_drop_columns[columns0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_drop_columns[columns1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_cov_corr_mixed[True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_astype_categoricals - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_astype_categoricals_known - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_gh_1301 - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_column_assignment - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_array_assignment - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_columns_assignment - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_attribute_assignment - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_setitem_triggering_realign - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_inplace_operators - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_copy - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-disk-split_every] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-disk-split_out] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_cumulative_multiple_columns - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-tasks-split_every] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_setitem - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_reduction_split[median-tasks-split_out] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_setitem_with_bool_series_as_key - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_dtype_cast - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_assign_na_float_columns - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_assign_no_warning_fragmented - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_dot - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_dot_nan - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_transform_getitem_works[max] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_dataframe.py::test_transform_getitem_works[sum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_on_index[disk-sync] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_on_index[disk-threads] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_on_index[tasks-sync] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_on_index[tasks-threads] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-key1-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_apply_tasks[tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-a-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumcount-key1-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-a-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumsum-key1-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-a-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumprod-key1-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-a-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[tasks-cumcount-key1-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_multi_character_column_name[disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_groupby_multi_character_column_name[tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_unaligned_index[disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_unaligned_index[tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[disk-cumsum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[disk-cumprod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[tasks-cumsum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_dataframe_cum_caching[tasks-cumprod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-cumprod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-cumcount] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[disk-cumsum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-cumprod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-cumcount] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_agg_funcs[tasks-cumsum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-min-group_args0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-min-group_args2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-min-idx] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-mean-group_args0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-mean-group_args2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-mean-idx] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-<lambda>-group_args0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-<lambda>-group_args2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[disk-<lambda>-idx] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-min-group_args0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-min-group_args2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-1-<lambda>4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-min-idx] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-mean-group_args0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-mean-group_args2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-mean-idx] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-<lambda>-group_args0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-<lambda>-group_args2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_column_and_index_apply[tasks-<lambda>-idx] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-disk-2-<lambda>4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-1-<lambda>4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[disk-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[disk-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[disk-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_dataframe_aggregations_multilevel[median-tasks-2-<lambda>4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[tasks-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[tasks-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_group_keys[tasks-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_shift_with_freq[disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_shift_with_freq[tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column0-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column0-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column1-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column1-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column2-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[disk-column2-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column0-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column0-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column1-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column1-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column2-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_split_out_multiindex[tasks-column2-3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on2-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_known[on3-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on2-disk-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on2-tasks-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on3-disk-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_single_to_known[on3-tasks-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[idx-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on1-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on2-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_unknown[on3-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-1-<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-1-<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-1-<lambda>2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-2-<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-2-<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[idx-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-disk-2-<lambda>2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-1-<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-1-<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-1-<lambda>2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-2-<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-2-<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on1-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_series_aggregations_multilevel[median-tasks-2-<lambda>2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on2-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_known[on3-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[idx-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on1-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on2-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-inner-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-inner-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-left-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-left-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-right-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-right-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-outer-disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_unknown_to_unknown[on3-outer-tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_join[tasks-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_join[tasks-outer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_join[disk-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_join[disk-outer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-a-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumsum-key1-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-disk-True-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-a] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-disk-0.75-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-c] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-tasks-True-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-d] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on2-tasks-0.75-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-sel3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-disk-True-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-disk-0.75-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_cumulative[disk-cumprod-a-sel4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-tasks-True-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_merge_column_and_index.py::test_merge_known_to_double_bcast_left[on3-tasks-0.75-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_methods.py::test_assign_not_modifying_array_inplace - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-observed-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-disk-unobserved-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-observed-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[min-tasks-unobserved-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-observed-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_asof_with_empty - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_concat_with_operation_remains_hlg - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-disk-unobserved-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-inner] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-outer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-left] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_columns_dtypes[True-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-observed-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge[disk-outer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge[disk-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[max-tasks-unobserved-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge[tasks-outer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge[tasks-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[disk-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[disk-outer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[tasks-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_empty_left_df[tasks-outer] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-disk-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[count-tasks-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-observed-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-disk-unobserved-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-observed-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[size-tasks-unobserved-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_concat_categorical[False-True-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_concat_categorical[False-False-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_concat_categorical[False-False-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_concat_categorical_mixed_simple - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_concat_datetimeindex - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_singleton_divisions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_repartition_repeated_divisions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_multi_duplicate_divisions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_concat_ignore_order[True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_concat_ignore_order[False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_nullable_types_merge[Int64] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_nullable_types_merge[Float64] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_nullable_types_merge[int64[pyarrow]] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_nullable_types_merge[float64[pyarrow]] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_categorical_join - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_categorical_merge_with_merge_column_cat_in_one_and_not_other_upcasts - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_categorical_merge_retains_category_dtype - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-28-inner] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-28-left] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-28-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-32-inner] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-32-left] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[lg-32-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-28-inner] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-28-left] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-28-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-32-inner] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-32-left] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_multi.py::test_merge_tasks_large_to_small[sm-32-right] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-observed-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-disk-unobserved-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-observed-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[first-tasks-unobserved-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-observed-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-disk-unobserved-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-observed-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_reshape.py::test_get_dummies_categories_order - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_reshape.py::test_get_dummies_errors - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-cat_1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-ordered-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_aggregate_categorical_observed[last-tasks-unobserved-groupby1-unordererd-unknown] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_reshape.py::test_pivot_table_errors - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-cumprod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[disk-cumsum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-cumprod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_groupby.py::test_groupby_numeric_only_true[tasks-cumsum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-x-pandas] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-z-pandas] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-by2-pandas] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[False-by3-pandas] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-True-first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-True-last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-False-first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-a-False-last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-True-first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-True-last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-False-first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-b-False-last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-True-first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-True-last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-False-first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data0-5-by2-False-last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_general[disk-4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-True-first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_general[disk-7] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-True-last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_general[tasks-4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-False-first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_general[tasks-7] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-a-False-last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[disk-string[python]] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-b-True-first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[disk-string[pyarrow]] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[disk-object] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-b-True-last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[tasks-string[python]] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-True-first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[tasks-string[pyarrow]] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-True-last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_string[tasks-object] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-False-first] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_names[tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_with_nulls[data1-5-by2-False-last] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_2[disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_shuffle_by_as_list - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_2[tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_custom_function[10-by0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_3[disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_custom_function[10-by1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_3[tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[True-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_bool_ascending - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[True-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_timestamp[3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[False-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data5-date32[pyarrow]] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_no_sort[False-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_pyarrow_dtype[data9-time64[ns][pyarrow]] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_shuffle_sort[disk] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_shuffle_nulls_introduced - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_shuffle_sort[tasks] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_with_explicit_divisions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_divisions_2 - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index[pandas] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_datetime_precision[ns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_datetime_precision[us] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_drop[True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_drop[False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_empty_partition - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[int] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[float] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[str] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_on_empty[<lambda>] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_empty_partitions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_set_index_timestamp - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_disk_shuffle_check_actual_compression - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_utils_dataframe.py::test_assert_eq_sorts - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_compute_current_divisions_nan_partition - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[10-a-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[10-b-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[10-by2-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[500-a-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[500-b-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values[500-by2-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tseries/tests/test_resample.py::test_resample_pads_last_division_to_avoid_off_by_one - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-z-pandas] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tests/test_shuffle.py::test_sort_values_tasks_backend[True-by3-pandas] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
FAILED dask/dataframe/tseries/tests/test_resample_expr.py::test_resample_agg - _pickle.PicklingError: Can't pickle local object <function test_resample_ag...
FAILED dask/tests/test_tokenize.py::test_tokenize_pandas - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rdiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rtruediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rfloordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rpow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-add-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-sub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-div-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-divide-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-truediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-floordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-mod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-pow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-radd-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rsub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rdiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rtruediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rfloordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rmod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-False-rpow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-add-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-sub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-div-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-divide-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-truediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-floordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-mod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-pow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-radd-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rsub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rdiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rtruediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rfloordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rmod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-True-rpow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-add-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-sub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-div-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-divide-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-truediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-floordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-mod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-pow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-radd-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_align_partitions.py::test_broadcasting_scalar[__add__] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_align_partitions.py::test_broadcasting_scalar[add] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_align_partitions.py::test_assign_unknown_partitions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_del - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-None-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-None-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-None-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-StringIO-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-StringIO-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[True-StringIO-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rsub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-None-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-None-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-None-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-StringIO-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-StringIO-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[False-StringIO-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-None-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-None-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-None-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-StringIO-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-StringIO-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_info[None-StringIO-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dont_fuse_from_pandas_ops - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_column_projection_modify_list - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_setitem - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_set_index_blockwise_culling - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_explode_simplify - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dask - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cumsum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cumprod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cummin] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_cumulative_methods[cummax] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_bool - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_empty_columns - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nbytes - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_pop - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_value_counts - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dropna - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index_projections - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-None-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-1h-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-1h-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-freq2-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[index-freq2-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-None-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-1h-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-1h-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-freq2-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[0-freq2-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-None-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rdiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-1h-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-1h-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-freq2-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[columns-freq2-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-None-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-1h-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-1h-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-freq2-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift[1-freq2-2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_memory_usage - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nlargest_nsmallest[func0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nlargest_nsmallest[func1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>6] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>7] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>8] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>9] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>10] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>11] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>12] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>13] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rtruediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>14] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>15] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>16] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>17] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>18] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>19] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_conditionals[<lambda>20] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[1-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[2-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_diff[None-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rfloordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rmod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-add-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[dataframe-False-rpow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-sub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-add-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-sub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-div-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-divide-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-truediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-floordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-mod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-pow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-radd-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rsub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_iloc - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_iloc_errors - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_slice - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_iloc_slice - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[False-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[False-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[True-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_columns_dtype_on_empty_slice[True-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_loc - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_with_series - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_with_array - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_loc_with_function - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_indexing.py::test_reverse_indexing - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_broadcast - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap[1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap[4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[series-True-rmul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_divisions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_partitions_partition_info - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_errors - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-div-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-True-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-True-False-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-True-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup0-False-False-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-True-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-divide-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-True-False-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-True-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup1-False-False-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-True-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-truediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-True-False-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-True-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup2-False-False-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-True-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-True-False-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-floordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-True-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-True-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_map_overlap_multiple_dataframes[overlap_setup3-False-False-False-4-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_map_partitions_overlap.py::test_token_given - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-mod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-pow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-radd-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rsub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rdiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rtruediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rfloordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_predicate_pushdown.py::test_rewrite_filters - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_predicate_pushdown.py::test_rewrite_filters_query - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_median - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_min_dt - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[1-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[1-5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[True-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[True-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_drop_duplicates[True-5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[1-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[1-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[1-5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[True-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rmod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[True-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts[True-5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts_sort - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[1-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[1-5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[True-None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unique[True-5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-sum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-prod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-min] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-max] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-any] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-all] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[False-21-count] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-sum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-prod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-min] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-max] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-any] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-all] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[None-23-count] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-sum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-prod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-min] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-max] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-any] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-all] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[5-23-count] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-sum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-prod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-min] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-max] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-any] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-all] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_split_every[2-31-count] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[False-53] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[None-57] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[5-57] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-True-rpow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_dataframe_mode_split_every[2-73] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-sum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-prod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-min] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-max] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-any] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-all] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-mode] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[False-31-count] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-sum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-prod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-min] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-max] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-any] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-all] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-mode] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[None-33-count] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-sum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-prod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-min] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-max] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-any] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-all] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-mode] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[5-33-count] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-sum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-prod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-min] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-max] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-any] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-all] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-mode] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_series_split_every[2-41-count] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[sum--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[sum-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-add-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[sum-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[prod--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[prod-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[prod-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[min--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[min-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[min-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[max--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[max-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[max-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[any--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[any-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[any-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[all--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[all-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[all-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[mode--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[mode-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[mode-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[count--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[count-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[count-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[nunique_approx--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[nunique_approx-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2[nunique_approx-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[drop_duplicates--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[drop_duplicates-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[drop_duplicates-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[unique--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[unique-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[unique-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[value_counts--1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[value_counts-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_split_every_lt2_split_out[value_counts-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[None] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-sub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_nunique_approx[10] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unique_base - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_value_counts_split_out_normalize - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func6] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func7] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func8] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func9] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func10] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func11] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func12] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_reductions[func13] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[func3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_index_reductions[<lambda>] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[<lambda>] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func6] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_unimplemented_on_index[func7] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_cov_corr - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_reductions.py::test_mean_series_axis_none - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-div-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-divide-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-truediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-floordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-mod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-pow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-radd-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rsub-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmul-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rdiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_ufunc - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[mean] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[std] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[sum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[cumsum] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[cumprod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[var] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[min] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[max] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[all] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[any] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_reducers[prod] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_ufunc.py::test_clip - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rtruediv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rfloordiv-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rmod-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-None-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-None-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-0-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-columns] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_method_operators[1-False-rpow-1-x-0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_binary_operator[<lambda>4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_and_or[<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_and_or[<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_to_timestamp[start] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_to_timestamp[end] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>6] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>7] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>8] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>9] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>10] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>11] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>12] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>13] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>14] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>15] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>16] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>17] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>18] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>19] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>20] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>21] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>22] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>23] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>24] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>25] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>26] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>27] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>28] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>29] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>30] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>31] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>32] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>33] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>34] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>35] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>36] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>37] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>38] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>39] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>40] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>41] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>42] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>43] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>44] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>45] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>46] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise[<lambda>47] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_select_dtypes_projection - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_rename - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_mask_where - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_isna - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_clear_divisions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_to_numeric - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_to_timedelta - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_not_implemented - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only[<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only[<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_blockwise_pandas_only_warning[<lambda>3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_map_meta - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_add_suffix_add_prefix - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_rename_axis - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_series_name - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_isin - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_isin_repr - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_round - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repr - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_combine_first_simplify - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_rename_traverse_filter - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_columns_traverse_filters - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_clip_traverse_filters - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-zz0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-projection1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-projection2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[x-zz1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-zz0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-projection1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-projection2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_subset_simplify[subset1-zz1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_broadcast - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_persist - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_index - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index[True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index[False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head_down - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_case_when - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head_head - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head_npartitions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head_tail_repartition - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_tail - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_tail_down - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_tail_tail - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_tail_repartition - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_projection_stacking - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_projection_stacking_coercion - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_projection_pushdown_dim_0 - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_remove_unnecessary_projections - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_substitute_parameters - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_copy - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_partitions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_get_partition - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_column_getattr - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_serialization - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_size_optimized - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_series_iter - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dataframe_iterrows - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dataframe_itertuples - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_array_assignment - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_setattr - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_rename_projection_no_renamer - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index_projection_drop - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[list] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[array] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[Series] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_getitem_column_types[Index] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_random_partitions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_depth - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_partitions_nested - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[7-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[7-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[12-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_npartitions[12-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_divisions[True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_divisions[False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_no_op - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_repartition_partition_size - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_len - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_astype_simplify - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates[1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates[True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_split_out - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_walk - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_find_operations - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dropna_simplify[x] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dropna_simplify[subset1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dir - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-replace-args0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-isin-args1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-clip-args2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-isna-args3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-round-args4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-abs-args5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[x-fillna-args6] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-replace-args0] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-isin-args1] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-clip-args2] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-isna-args3] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-round-args4] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-abs-args5] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_simplify_up_blockwise[indexer1-fillna-args6] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_isin_as_predicate - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_sample - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_align - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nunique[False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nunique[True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_nunique_approx - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_memory_usage_per_partition - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_simplify - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_simplify_new_column_not_needed - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_simplify_series - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_non_series_inputs - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_squash_together - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_rename_series_reduction - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_are_co_aligned - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_pandas_inputs - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_astype_categories - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_simplify - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_can_co_align - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_mixed_array_op - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_len_shuffle_repartition - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_columns_setter - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_contains - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_filter_pushdown_unavailable - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_filter_pushdown - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shape - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_size - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_duplicates_groupby - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_expression_bool_raises - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_expr_is_scalar - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_quantile_frame - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_quantile - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dtype - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_datetime[B-False] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_datetime[D-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_datetime[h-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_isin_head - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_head_npartitions_divisions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_period_index[D-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_period_index[h-True] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_TimedeltaIndex[min] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_TimedeltaIndex[D] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shift_with_freq_TimedeltaIndex[h] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_iter - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_items - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_predicate_pushdown_ndim_change - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_axes - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_scalar_repr - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_array_conversion - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/tests/test_accessors.py::test_dt_accessor - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_reset_index_filter_pushdown - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/tests/test_accessors.py::test_dt_accessor_not_available - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_astype_filter_pushdown - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_not_available - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_drop_set - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_getitem - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_shape_integer - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_extractall - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_removeprefix_removesuffix[removeprefix] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_removeprefix_removesuffix[removesuffix] - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/tests/test_accessors.py::test_str_accessor_cat - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_assign_overwriting_column - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_collection.py::test_dropna_merge - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/tests/test_accessors.py::test_string_nullable_types - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_str - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_pdf - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_divisions - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_one_object - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_one_no_columns - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_simplify - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_simplify_projection_not_added - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_axis_one_co_aligned - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_axis_one_all_divisions_unknown - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_axis_one_drop_dfs_not_selected - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_index - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_one_series - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_series - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_concat.py::test_concat_series_and_projection - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_simple - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_with_non_fusable_on_top - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_optimize_fusion_repeat - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_optimize_fusion_broadcast - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_persist_with_fusion - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
ERROR dask/dataframe/dask_expr/tests/test_fusion.py::test_name - FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
= 871 failed, 11843 passed, 1232 skipped, 282 xfailed, 263 xpassed, 594 warnings, 3116 errors, 20 rerun in 802.13s (0:13:22) =
error: Bad exit status from /var/tmp/rpm-tmp.txOTKE (%check)
    Bad exit status from /var/tmp/rpm-tmp.txOTKE (%check)

RPM build errors:
Finish: rpmbuild python-dask-2025.4.1-3.fc43~bootstrap.src.rpm
Finish: build phase for python-dask-2025.4.1-3.fc43~bootstrap.src.rpm
INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan
INFO: /var/lib/mock/fedora-rawhide-x86_64-1753885010.838369/root/var/log/dnf5.log
INFO: chroot_scan: creating tarball /var/lib/copr-rpmbuild/results/chroot_scan.tar.gz
/bin/tar: Removing leading `/' from member names
ERROR: Exception(/var/lib/copr-rpmbuild/results/python-dask-2025.4.1-3.fc43~bootstrap.src.rpm) Config(fedora-rawhide-x86_64) 14 minutes 7 seconds
INFO: Results and/or logs in: /var/lib/copr-rpmbuild/results
INFO: Cleaning up build root ('cleanup_on_failure=True')
Start: clean chroot
INFO: unmounting tmpfs.
Finish: clean chroot
ERROR: Command failed: 
 # /usr/bin/systemd-nspawn -q -M 012f2554ace84a1d874cec2d42295461 -D /var/lib/mock/fedora-rawhide-x86_64-1753885010.838369/root -a -u mockbuild --capability=cap_ipc_lock --rlimit=RLIMIT_NOFILE=10240 --capability=cap_ipc_lock --bind=/tmp/mock-resolv.syevb29e:/etc/resolv.conf --bind=/dev/btrfs-control --bind=/dev/mapper/control --bind=/dev/fuse --bind=/dev/loop-control --bind=/dev/loop0 --bind=/dev/loop1 --bind=/dev/loop2 --bind=/dev/loop3 --bind=/dev/loop4 --bind=/dev/loop5 --bind=/dev/loop6 --bind=/dev/loop7 --bind=/dev/loop8 --bind=/dev/loop9 --bind=/dev/loop10 --bind=/dev/loop11 --console=pipe --setenv=TERM=vt100 --setenv=SHELL=/bin/bash --setenv=HOME=/builddir --setenv=HOSTNAME=mock --setenv=PATH=/usr/bin:/bin:/usr/sbin:/sbin '--setenv=PROMPT_COMMAND=printf "\033]0;<mock-chroot>\007"' '--setenv=PS1=<mock-chroot> \s-\v\$ ' --setenv=LANG=C.UTF-8 --resolv-conf=off bash --login -c '/usr/bin/rpmbuild -ba --noprep  --target x86_64 /builddir/build/originals/python-dask.spec'

Copr build error: Build failed